Suppression of gastric cancer cell proliferation by miR-494-3p inhibitor-loaded engineered exosomes.

Background: Gastric cancer necessitates novel treatments, and exosomes are promising therapeutic carriers. We created miR-494-3p inhibitor exosomes to assess their effects on gastric cancer cells. Methods: We conducted a comprehensive investigation into the expression of the oncogenic miR-494-3p in gastric cancer tissues from patients. Subsequently, we engineered miR-494-3p inhibitor-loaded exosomes and characterized their morphology and size through transmission electron microscopy and nanoparticle tracking analysis. We next determined the encapsulation efficiency of the miR-494-3p inhibitor within these exosomes and evaluated the exosomes' structural integrity by quantifying the presence of exosomal markers. Following these validations, we co-cultured miR-494-3p inhibitor exosomes with cancer cells and employed PKH26 staining to visualize the efficient endocytosis of engineered exosomes by gastric cancer cells and assess the impact of these modified exosomes on gastric cancer cell proliferation, apoptosis, migration, and invasion. Results: Increased expression of miR-494-3p was observed in gastric cancer tissues as compared to controls. Significant low miR-494-3p levels were found within miR-494-3p inhibitor exosomes, signifying effective encapsulation. The incorporation of miR-494-3p inhibitor into engineered exosomes did not alter exosome morphology or size. Finally, PKH26-stained exosomes clearly demonstrated efficient endocytosis by gastric cancer cells, leading to reduced proliferation, migration, invasion, and increased apoptosis. Conclusion: Our study identifies elevated miR-494-3p in gastric cancer tissues prompting the development of miR-494-3p inhibitor-loaded exosomes with efficient encapsulation. These engineered exosomes demonstrate successful endocytosis by cancer cells. This highlights their potential for therapeutic use in gastric cancer treatment by suppressing proliferation, migration, and invasion while enhancing apoptosis.

Perioperative versus adjuvant S-1 plus oxaliplatin chemotherapy for stage II/III resectable gastric cancer (RESONANCE): a randomized, open-label, phase 3 trial.

Evidence from Europe shows that perioperative chemotherapy may be beneficial for the treatment of locally advanced gastric cancer, but reliable and robust data is lacking. To rectify this, the phase 3 RESONANCE trial investigated the efficacy and safety of S-1 plus oxaliplatin (SOX) as a perioperative chemotherapy regimen for gastric cancer. This randomized, open-label trial enrolled patients from 19 medical centers with stage II/III resectable gastric cancer who were centrally randomly assigned to either perioperative chemotherapy (PC) arm or adjuvant chemotherapy (AC) arm. Patients in the PC arm received two to four cycles of SOX followed by surgery and four to six cycles of SOX. Patients in the AC arm received upfront surgery and eight cycles of SOX. 386 patients in each group were enrolled and 756 (382 in PC and 374 in AC) were included in the mITT population. The three-year DFS rate was 61.7% in the PC arm and 53.8% in the AC arm (log-rank p = 0.019). The R0 resection rate in the PC arm was significantly higher than that in the AC arm (94.9% vs. 83.7%, p < 0.0001). There was no difference between two arms in surgical outcomes or postoperative complications. Safety-related data were like the known safety profile. In conclusion, from a clinical perspective, this trial indicated a trend towards higher three-year disease-free survival rate with perioperative SOX in stage II/III resectable gastric cancer with well-tolerated toxicity compared to adjuvant SOX, which might provide a theoretical basis for applying perioperative SOX in advanced gastric cancer patients. (ClinicalTrials.gov NCT01583361).

Machine learning methods predict recurrence of pN3b gastric cancer after radical resection.

Background: The incidence of stage pN3b gastric cancer (GC) is low, and the clinical prognosis is poor, with a high rate of postoperative recurrence. Machine learning (ML) methods can predict the recurrence of GC after surgery. However, the prognostic significance for pN3b remains unclear. Therefore, we aimed to predict the recurrence of pN3b through ML models. Methods: This retrospective study included 336 patients with pN3b GC who underwent radical surgery. A 3-fold cross-validation was used to partition the participants into training and test cohorts. Linear combinations of new variable features were constructed using principal component analysis (PCA). Various ML algorithms, including random forest, support vector machine (SVM), logistic regression, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and Gaussian naive Bayes (GNB), were utilized to establish a recurrence prediction model. Model performance was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Python was used for the analysis of ML algorithms. Results: Nine principal components with a cumulative variance interpretation rate of 90.71% were identified. The output results of the test set showed that random forests had the highest AUC (0.927) for predicting overall recurrence with an accuracy rate of 80.5%. Random forests had the highest AUC (0.940) for predicting regional recurrence with an accuracy of 89.7%. For predicting distant recurrence, random forests had the highest AUC (0.896) with an accuracy of 84.3%. For peritoneal recurrence, random forests had the highest AUC (0.923) with an accuracy of 83.3%. Conclusions: ML can personalize the prediction of postoperative recurrence in patients with GC with stage pN3b.

The Gastric Cancer Immune Prognostic Score (GCIPS) Shows Potential in Predicting an Unfavorable Prognosis for Gastric Cancer Patients Undergoing Immune Checkpoint Inhibitor Treatment.

(1) Background: This study aims to explore the predictive capability of the Gastric Cancer Immune Prognostic Score (GCIPS) for an unfavorable prognosis in gastric cancer patients undergoing immune checkpoint inhibitor (ICI) treatment. (2) Methods: This study included 302 gastric cancer patients who underwent treatment with ICIs at our institution from January 2017 to December 2022. The patients were randomly divided into a test set (201 cases) and a validation set (101 cases) using a random number table. Kaplan-Meier survival analysis and the log-rank test were used to investigate survival differences. Cox regression analysis and Lasso regression analysis were employed to establish the GCIPS and identify independent prognostic indicators. ROC curves, time-ROC curves, and nomograms were utilized to further explore the predictive performance of GCIPS. (3) Results: The test set and validation set showed no statistical differences in clinical and pathological features, as well as blood parameters (all p > 0.05). Cox regression analysis revealed that white blood cells (WBC), lymphocytes (LYM), and the international normalized ratio (INR) emerged as independent prognostic blood indicators after eliminating collinearity through Lasso analysis. The GCIPS was established using ß coefficients with the following formula: GCIPS = WBC (109/L) × 0.071 - LYM (109/L) × 0.375 + INR × 2.986. ROC curves based on death and time-ROC curves demonstrated that the GCIPS had higher AUCs than other classical markers at most time points. Survival analyses of all subgroups also revealed a significant correlation between the GCIPS and patients' progression-free survival (PFS) and overall survival (OS) (all p < 0.05). Furthermore, the GCIPS was identified as an independent prognostic factor for both PFS and OS. Analyses in the validation set further confirmed the reliability and stability of the GCIPS in predicting patient prognosis. Finally, nomograms incorporating the GCIPS exhibited high accuracy in both the test and validation sets. Additionally, the nomograms revealed that the GCIPS had a higher prognostic value than any other factor, including the TNM stage. (4) Conclusions: The GCIPS demonstrated its ability to predict adverse outcomes in gastric cancer patients undergoing ICIs treatment and had a high prognostic value. As a readily accessible and simple novel biomarker, it effectively identified high-risk patients.

Evaluating the influence of sarcopenia and myosteatosis on clinical outcomes in gastric cancer patients undergoing immune checkpoint inhibitor.

BACKGROUND: The development and progression of gastric cancer (GC) are closely linked to the nutritional status of patients. Although immunotherapy has been demonstrated to be clinically effective, the relationships of sarcopenia and myosteatosis with the use of immune checkpoint inhibitors (ICIs) in patients with gastric cancer remain to be characterized. AIM: To assess the effects of sarcopenia and myosteatosis on the clinical outcomes of patients with GC undergoing treatment with an ICI. METHODS: We performed a retrospective study of patients who were undergoing immunotherapy for GC. For the evaluation of sarcopenia, the optimal cut-off value for the skeletal muscle index was established using receiver operating characteristic analysis of data obtained from pre-treatment computed tomography images at the L3 vertebral level. Myosteatosis was defined using the mean skeletal muscle density (SMD), with a threshold value of < 41 Hounsfield units (HU) for patients with a body mass index (BMI) < 25 kg/m² and < 33 HU for those with a BMI ≥ 25 kg/m². The log-rank test was used to compare progression-free survival (PFS) and overall survival (OS), and a Cox proportional hazard model was used to identify prognostic factors. Nomograms were developed to predict the PFS and OS of patients on the basis of the results of multivariate analyses. RESULTS: We studied 115 patients who were undergoing ICI therapy for GC, of whom 27.4% had sarcopenia and 29.8% had myosteatosis. Patients with sarcopenia or myosteatosis had significantly shorter PFS and OS than those without these conditions. Furthermore, both sarcopenia and myosteatosis were found to be independent predictors of PFS and OS in patients with GC administering an ICI. The prediction models created for PFS and OS were associated with C-indexes of 0.758 and 0.781, respectively. CONCLUSION: The presence of sarcopenia or myosteatosis is a reliable predictor of the clinical outcomes of patients with GC who are undergoing treatment with an ICI.

Body composition change indices combined with Prognostic Nutritional Index predicts the clinical outcomes of patients with gastric cancer treated with immune checkpoint inhibitor.

OBJECTIVE: This study aimed to investigate the prognostic significance of the Prognostic Nutritional Index (PNI) in conjunction with body composition change indices, namely subcutaneous fat area (SFA) and skeletal muscle index (SMI), with regard to clinical outcomes in patients with gastric cancer (GC) undergoing immune checkpoint inhibitors (ICIs) treatment. METHODS: This retrospective investigation encompassed patients with comprehensive clinical and pathological data, inclusive of portal phase enhanced CT images. Continuous variables underwent analysis utilizing the Student t-test or Mann-Whitney U-test, while categorical variables were assessed employing the Pearson chi-squared test or Fisher test. Survival outcomes were evaluated using Kaplan-Meier survival curves and the Log-rank test. Independent prognostic indicators were determined through Cox regression analysis, and a nomogram predicting survival probability for progression-free survival (PFS) and overall survival (OS) was constructed. RESULTS: Within the PNI-SFA groups, patients in Group 1 exhibited inferior PFS and OS compared to the other two groups. Similarly, among the PNI-SMI groups, Group 1 patients demonstrated poorer PFS and OS. PNI-SMI and Eosi were identified as independent prognostic factors through Cox regression analysis. Furthermore, positive associations with patient prognosis were observed for BMI, SAF, SMI, and PNI. CONCLUSION: The comprehensive consideration of PNI-SFA and PNI-SMI proved to be a superior prognostic predictor for GC patients undergoing ICI treatment.

Prognostic Value of Fibrinogen to Prealbumin Ratio (FPR) in Resectable Gastric Cancer.

Background: The ratio of fibrinogen to prealbumin (FPR) is associated with the prognosis of many cancers. However, the prognostic significance of FPR in resectable gastric cancer has not been clarified. Methods: A total of 760 patients with resectable gastric cancer participated in this study. The receiver operating characteristic curve (ROC) was used to calculate the optimal cutoff value of each immunonutrition marker. Univariate and multivariate Cox regression analyses were used to confirm the prognostic value of FPR in patients with gastric cancer and to select appropriate variables for the construction of nomogram. Results: Utilizing ROC analysis, we calculated the optimal cutoff value for FPR and stratified 760 patients into high and low FPR groups. Subsequent examination revealed notable distinctions in baseline characteristics between these groups. For instance, Patients with higher FPR tend to be older and have more lymph node metastasis. Statistical analysis through the chi-square test confirmed the significance of these differences (P < 0.05). In addition, the results of the multivariate Cox proportional hazards regression analysis indicate that the factors related to OS were age (P = 0.001), T stage (P < 0.001), N stage (P < 0.001), radical resection (P < 0.001), and FPR (P < 0.024). The nomogram is composed of the above five variables. ROC analysis showed that the area under the curve (AUC) of the nomogram was 0.859 (95% CI: 0.831-0.887), and the sensitivity and specificity were 77.4% and 82.1%, respectively. Conclusion: FPR is a potential marker in patients with resectable gastric cancer. The nomogram based on FPR shows good predictive ability, which is helpful for clinicians to judge the prognosis of patients and choose targeted treatment strategies.

Clinical significance of systemic inflammation response index and platelet-lymphocyte ratio in patients with adenocarcinoma of the esophagogastric junction and upper gastric cancer.

Background: Tumor immunity plays an important role in assessing the tumor progression. The purpose of this study was to investigate the prognostic value of combined systemic inflammation response index (SIRI) and platelet-lymphocyte ratio (PLR) of gastroesophageal junction cancer (AEG) and upper gastric cancer (UGC) patients. Methods: In this retrospective study, patients from 2003 to 2014 were divided into training and validation sets. The prognostic accuracy of each variable was compared using time-independent ROC analysis. The scoring system was calculated by cut-off values of SIRI and PLR in 5-year. Kaplan-Meier and Log-rank tests were used to analyze overall survival (OS). Chi-square test was used to analyze the association between clinical characteristics and the scoring system. Univariate and multivariate analyses based on the competitive risk regression model were used to analyze independent predictors of death due to AGC and UGC. R software was used to construct the Nomogram model of risk assessment. Results: Patients with SIRI-PLR = 2 had worse survival time than those with 0 and 1 (P < 0.001) and more suitable for postoperative adjuvant chemotherapy (P = 0.002). High PLR patients were more suitable for proximal gastrectomy (P = 0.049). SIRI-PLR were independent predictors in training set (P < 0.001), which could be combined with age, pTNM stage and postoperative chemotherapy to construct Nomogram for predicting OS. Conclusions: Preoperative SIRI-PLR score was an independent predictor for patients with AEG and UGC. The Nomogram model constructed by age, SIRI-PLR, pTNM stage and postoperative chemotherapy can correctly predict the prognosis of patients.

A prognostic marker LTBP1 is associated with epithelial mesenchymal transition and can promote the progression of gastric cancer.

LTBP1 is closely related to TGF-ß1 function as an essential component, which was unclear in gastric cancer (GC). Harbin Medical University (HMU)-GC cohort and The Cancer Genome Atlas (TCGA) dataset were combined to form a training cohort to calculate the connection between LTBP1 mRNA expression, prognosis and clinicopathological features. The training cohort was also used to verify the biological function of LTBP1 and its relationship with immune microenvironment and chemosensitivity. In the tissue microarrays (TMAs), immunohistochemical (IHC) staining was performed to observe LTBP1 protein expression. The correlation between LTBP1 protein expression level and prognosis was also analyzed, and a nomogram model was constructed. Western blotting (WB) was used in cell lines to assess LTBP1 expression. Transwell assays and CCK-8 were employed to assess LTBP1's biological roles. In compared to normal gastric tissues, LTBP1 expression was upregulated in GC tissues, and high expression was linked to a bad prognosis for GC patients. Based on a gene enrichment analysis, LTBP1 was primarily enriched in the TGF-ß and EMT signaling pathways. Furthermore, high expression of LTBP1 in the tumor microenvironment was positively correlated with an immunosuppressive response. We also found that LTBP1 expression (p = 0.006) and metastatic lymph node ratio (p = 0.044) were independent prognostic risk factors for GC patients. The prognostic model combining LTBP1 expression and lymph node metastasis ratio reliably predicted the prognosis of GC patients. In vitro proliferation and invasion of MKN-45 GC cells were inhibited and their viability was decreased by LTBP1 knockout. LTBP1 plays an essential role in the development and progression of GC, and is a potential prognostic biomarker and therapeutic target for GC.

Normalization weighted combination scores re-evaluate TNM staging of gastric cancer: a retrospective cohort study based on a multicenter database.

BACKGROUND: The pathological depth of tumor invasion (pT) and lymph node metastasis (pN) are critical independent prognostic factors for patients with gastric cancer (GC), representing effective methods for evaluating prognosis. In this study, the authors employed a normalization weight combination score to calculate the weight ratio of the pT stage and pN stage. Subsequently, the authors established a novel weighted TN (wTN) staging model based on these T and N weights, evaluating its prognostic capacity. METHODS: This study utilized a training cohort from A Medical University Cancer Hospital and a validation cohort from the SEER database. Least absolute shrinkage and selection operator (LASSO) and Cox regression were employed to screen clinical characteristics. Multivariate linear regression and cluster analysis calculated the weight ratio of T stage and N stage in the training and validation cohorts, respectively, followed by re-staging. Prognostic value was evaluated using C-index, likelihood ratio, Wald, and Score tests for wTN stage and tumor-node-metastasis (TNM) stage. A nomogram model was developed, and accuracy was assessed using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and restricted cubic spline (RCS) analyses. RESULTS: LASSO was used for initial screening, selecting eight potential features for Cox analysis. Age, tumor size, metastasis lymph nodes (MLNs), and tumor location were confirmed as independent prognostic factors. wTN was calculated in the training and validation cohorts, and nomograms were established with the independent factors. N stage had a higher weight proportion than T stage in both cohorts (0.625/0.375 in training cohort, 0.556/0.444 in validation cohort). wTN outperformed the 8th TNM stage in C-index, likelihood ratio, Wald, and Score tests in the training cohort, with successful validation in the validation cohort. Stratified analysis of distinct pathological types further demonstrates that wTN staging exhibits superior prognostic performance. CONCLUSION: The wTN staging model based on T stage and N stage weights has a good prognostic value for GC patients. The same conclusion was obtained in different pathological stratification.

Development and Validation of Tumor Marker Indices in Advanced Gastric Cancer Patients.

BACKGROUND: Tumor markers (TMs) are important for the prognosis of gastric cancer (GC). However, the prognostic importance of the tumor marker index (TMI) based on GC-specific TMs for advanced gastric cancer (AGC) still needs to be further explored. METHODS: We retrospectively examined patients who underwent radical gastric cancer surgery between February 2014 and June 2016 at the Department of Gastroenterological Surgery, Affiliated Cancer Hospital, Harbin Medical University. The patients were divided into training and validation groups. TMI was determined as the geometric mean of the standard cancer antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) levels. Patient overall survival was assessed using the Kaplan-Meier method. Independent prognosis-associated risk factors were identified using Cox hazard regression models. A nomogram model incorporating TMI and clinicopathological factors was developed, and its performance was evaluated using a decision curve analysis, concordance index, and calibration plots. RESULTS: In the TMI training cohort, the cutoff value was set at .439, categorizing patients into TMI-High and TMI-Low groups. The 5-year survival rate in the TMI-Low group significantly surpassed that in the TMI-High group (78.2% vs 58.1% and 49.7 vs 41.6, P < .001). TMI emerged as an independent prognostic factor. The nomogram accurately predicted patient prognosis by using TMI and clinicopathological characteristics. Validation of the TMI in the independent cohort yielded satisfactory results. CONCLUSION: The TMI constructed based on specific TMs associated with gastric cancer can offer a precise prognostic prediction for patients.

An oxidative stress biomarkers predict prognosis in gastric cancer patients receiving immune checkpoint inhibitor.

Objective: The development and advance of gastric cancer are inextricably linked to oxidative and antioxidant imbalance. Although immunotherapy has been shown to be clinically effective, the link between oxidative stress and gastric cancer patients treated with immune checkpoint inhibitor (ICIs) remains unknown. This study aims at looking into the prognostic value of oxidative stress scores in gastric cancer patients treated with ICIs. Methods: By taking the propagation to receiver operating characteristic (ROC) we got the best cut-off values, and divided 265 patients receiving ICIs and chemotherapy into high and low GC-Integrated Oxidative Stress Score (GIOSS) groups. We also used Kaplan-Meier and COX regression models to investigate the relationship between oxidative stress biomarkers and prognosis. Results: Through both univariate and multivariate analyses, it's shown that GIOSS severs as an independent prognostic factor for progression-free survival (PFS) and Overall survival (OS). Based on GIOSS cutoff values, patients with high GIOSS levels, compared to those with low levels exhibited shorter PFS and OS, both in the high GIOSS group, which performed poorly in the ICIs subgroup and other subgroup analyses. Conclusion: GIOSS is a biomarker that responds to systemic oxidative stress in the body and can predict prognosis in patients with gastric cancer who are taking ICIs. Additionally, it might come to medical professionals' aid in making more effective or more suitable treatment plans for gastric cancer.

Cancer-associated fibroblast expression of glutamine fructose-6-phosphate aminotransferase 2 (GFPT2) is a prognostic marker in gastric cancer.

Glutamine fructose-6-phosphate aminotransferase 2 (GFPT2) is a rate-limiting enzyme in hexosamine biosynthesis involved in the occurrence and progress of many cancers. What role it plays in gastric cancer (GC) is still unclear. In this study, transcriptome sequencing data from the Harbin Medical University (HMU)-GC cohort and The Cancer Genome Atlas (TCGA) dataset were combined with the HMU-TCGA training cohort to analyze the biological function and clinical significance of GFPT2. The correlation of GFPT2 with immune cells and stromal cells was analyzed in the GC immune microenvironment through transcriptome sequencing data and a public single-cell sequencing database. In cell lines, GC tissues, and the tissue microarray, GFPT2 protein expression was confirmed by western blotting and immunohistochemistry. The mRNA of GFPT2 was highly expressed in the tumor (p < 0.001), and GC cells and tumors expressed high levels of GFPT2 protein. Compared to low expression, high GFPT2 mRNA expression was associated with higher levels of tumor invasion, higher pathological stages, and poor prognosis (p = 0.02) in GC patients. In a drug susceptibility analysis, GFPT2 mRNA expression was associated with multiple chemotherapeutic drug sensitivity, including docetaxel, paclitaxel, and cisplatin. Gene enrichment analysis found that GFPT2 was mainly primarily involved in the extracellular matrix receptor interaction pathway. The ESTIMATE, CIBERSORT, and ssGSEA algorithms showed that GFPT2 was associated with immune cell infiltration. In addition, GFPT2 was more likely to be expressed within cancer-associated fibroblasts (CAFs), and high levels of GFPT2 expression were highly correlated with four CAFs scores (all p < 0.05). Finally, a prognostic model to assess the risk of death in GC patients was constructed based on GFPT2 protein expression and lymph node metastasis rate. In conclusion, GFPT2 plays an essential role in the function of CAFs in GC. It can be used as a biomarker to assess GC prognosis and immune infiltration.

Combined with prognostic nutritional index and IgM for predicting the clinical outcomes of gastric cancer patients who received surgery.

Objective: Although the survival rate of patients who undergo surgery for gastric cancer has greatly improved, still many patients have a poor prognosis. This retrospective study aimed to investigate the predictive ability of the PNI-IgM score, a combined prognostic nutritional index (PNI), and immunoglobulin M (IgM), on the prognosis of patients undergoing surgery for gastric cancer. Methods: 340 patients with gastric cancer who underwent surgery from January 2016 to December 2017 were selected. The PNI-IgM score ranged from 1 to 3: score of 1, low PNI (< 48.45) and low IgM (< 0.87); score of 2, low PNI and high IgM, or high PNI and low IgM; score of 3, high PNI and high IgM. We compared the differences in disease-free survival (DFS) and overall survival (OS) among the three groups, while univariate and multivariate analyses calculated prognostic factors for DFS and OS. In addition, the nomograms were constructed based on the results of multivariate analysis to estimate the 1-, 3- and 5-year survival probability. Results: There were 67 cases in the PNI-IgM score 1 group, 160 cases in the PNI-IgM score 2 group, and 113 cases in the PNI-IgM score 3 group. The median survival times of DFS in the PNI-IgM score group 1, the PNI-IgM score group 2, and the PNI-IgM score group 3 were 62.20 months, not reached, and not reached, and 67.57 months vs. not reached vs. not reached in three groups for OS. Patients in the PNI-IgM score group 1 had a lower DFS than the PNI-IgM score group 2 (HR = 0.648, 95% CI: 0.418-1.006, P = 0.053) and the PNI-IgM score group 3 (HR = 0.337, 95% CI: 0.194-0.585, P < 0.001). In stratified analysis, PNI-IgM score 1 had a worse prognosis in the age < 60 years group and CA724 < 2.11 U/m group. Conclusion: PNI-IgM score is a novel combination of nutritional and immunological markers that can be used as a sensitive biological marker for patients with gastric cancer who undergo surgery. The lower the PNI-IgM score, the worse the prognosis.

CD19 (+) B Cell Combined with Prognostic Nutritional Index Predicts the Clinical Outcomes of Patients with Gastric Cancer Who Underwent Surgery.

(1) Background: The aim of this study was to explore the predictive ability of lymphocyte subsets for the prognosis of gastric cancer patients who underwent surgery and the prognostic value of CD19 (+) B cell combined with the Prognostic Nutritional Index (PNI). (2) Methods: This study involved 291 patients with gastric cancer who underwent surgery at our institution between January 2016 and December 2017. All patients had complete clinical data and peripheral lymphocyte subsets. Differences in clinical and pathological characteristics were examined using the Chi-square test or independent sample t-tests. The difference in survival was evaluated using Kaplan-Meier survival curves and the Log-rank test. Cox's regression analysis was performed to identify independent prognostic indicators, and nomograms were used to predict survival probabilities. (3) Results: Patients were categorized into three groups based on their CD19 (+) B cell and PNI levels, with 56 cases in group one, 190 cases in group two, and 45 cases in group three. Patients in group one had a shorter progression-free survival (PFS) (HR = 0.444, p < 0.001) and overall survival (OS) (HR = 0.435, p < 0.001). CD19 (+) B cell-PNI had the highest area under the curve (AUC) compared with other indicators, and it was also identified as an independent prognostic factor. Moreover, CD3 (+) T cell, CD3 (+) CD8 (+) T cell, and CD3 (+) CD16 (+) CD56 (+) NK T cell were all negatively correlated with the prognosis, while CD19 (+) B cell was positively associated with the prognosis. The C-index and 95% confidence interval (CI) of nomograms for PFS and OS were 0.772 (0.752-0.833) and 0.773 (0.752-0.835), respectively. (4) Conclusions: Lymphocyte subsets including CD3 (+) T cell, CD3 (+) CD8 (+) T cell, CD3 (+) CD16 (+) CD56 (+) NK T cell, and CD19 (+) B cell were related to the clinical outcomes of patients with gastric cancer who underwent surgery. Additionally, PNI combined with CD19 (+) B cell had higher prognostic value and could be used to identify patients with a high risk of metastasis and recurrence after surgery.

Efficacy of Lymph Node Location-Number Hybrid Staging System on the Prognosis of Gastric Cancer Patients.

BACKGROUND: Lymph node metastasis location and number significantly affects the prognosis of patients with gastric cancer (GC). This study was designed to examine a new lymph node hybrid staging (hN) system to increase the predictive ability for patients with GC. METHODS: This study analyzed the gastrointestinal treatment of GC at the Harbin Medical University Cancer Hospital from January 2011 to December 2016, and selected 2598 patients from 2011 to 2015 as the training cohort (hN) and 756 patients from 2016 as the validation cohort (2016-hN). The study utilized the receiver operating characteristic curve (ROC), c-index, and decision curve analysis (DCA) to compare the prognostic performance of the hN with the 8th edition of AJCC pathological lymph node (pN) staging for GC patients. RESULTS: The ROC verification of the training cohort and validation cohort based on each hN staging and pN staging showed that for each N staging, the hN staging had a training cohort with an AUC of 0.752 (0.733, 0.772) and a validation cohort with an AUC of 0.812 (0.780, 0.845). In the pN staging, the training cohort had an AUC of 0.728 (0.708, 0.749), and the validation cohort had an AUC of 0.784 (0.754, 0.824). c-Index and DCA also showed that hN staging had a higher prognostic ability than pN staging, which was confirmed in the training cohort and the verification cohort, respectively. CONCLUSION: Lymph node location-number hybrid staging can significantly improve the prognosis of patients with GC.

Lymphocyte subset is more suitable than systemic inflammatory response biomarker and immunoglobulin in constructing prognostic nomogram model for advanced gastric cancer.

The serum factors of inflammation are known to be useful prognostic indicators of gastric cancer (GC). However, few studies have made comparisons to screen out more suitable biomarkers for the construction of Nomogram models. In this study, 566 patients who underwent radical gastrectomy were randomly selected. We evaluated the prognostic value of markers of systemic inflammation, including WBC, NLR, PLR, circulating total T cells, CD4+T cells, CD8+T cells and CD19+B cells, serum IgA, IgM, IgE and IgG, and compared them with traditional tumor markers (CEA, CA19-9, CA72-4 and CA125). Kaplan‒Meier analysis was used to analyze the correlation between biomarkers and overall survival (OS). We used time-dependent ROC analysis to investigate the prognostic accuracy of each biomarker. The risk of death was evaluated by the Cox regression model, and the Nomogram model was constructed by R software. We found that circulating total T cells, CD8+T cells, CEA, and CA125 had statistical significance in predicting advanced GC prognosis. Circulating CD8+T cells and CA125 were continuously superior to circulating total T cells and CEA in the prediction of 5-year OS. Cox regression found that CA125, circulating CD8+T cells, sex, and lymph node metastasis rate were independent risk factors for advanced GC. Furthermore, we combined all these predictors to construct a nomogram, which can supplement the AJCC 8th system. According to the comparison with commonly used serum immune biomarkers, circulating CD8+T cells is more sensitive to advanced GC. The prediction function of the Nomogram will supplement the traditional AJCC system, which contributes to individual survival prediction.

Nomogram Based on Preoperative Fibrinogen and Systemic Immune-Inflammation Index Predicting Recurrence and Prognosis of Patients with Borrmann Type III Advanced Gastric Cancer.

Background and Objectives: The prognosis is known to differ significantly among advanced gastric cancer (AGC) with Borrmann type III. This study aimed to evaluate the prognosis of these patients more individually. Methods: We selected 542 AGC patients with Borrmann type III. We used the receiver operating characteristic curve to analyze the cutoff values of inflammation indexes, and used Kaplan-Meier and Log rank tests to analyze recurrence-free survival (RFS) and overall survival (OS). The independent risk factors for recurrence and prognosis were analyzed by Cox proportional hazards regression model. The nomogram models were constructed by R studio. Results: Patients with high preoperative fibrinogen (F) and systemic immune-inflammation index (SII) levels had worse RFS and OS and higher risk of postoperative locoregional recurrence, hematogenous metastasis and lymph node metastasis. F and SII can combine with different clinicopathological features (all P<0.05) to construct nomograms to predict 5-year recurrence and prognosis, which both were superior to pTNM stage alone. Conclusion: The nomogram models based on F and SII can evaluate AGC with Borrmann type III postoperative recurrence and prognosis.

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study.

Background: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). Methods: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. Findings: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. Interpretation: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). Funding: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).