Journal of gastrointestinal oncology最新文献

{"title":"A study on screening of postoperative chemotherapy beneficial populations in stage II colorectal cancer based on diffusion weighted imaging and ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography.","authors":"Xuedong Wang, Lei Li, Linjie Wang, Weipeng Chen","doi":"10.21037/jgo-2026-0384","DOIUrl":"10.21037/jgo-2026-0384","url":null,"abstract":"<p><strong>Background: </strong>Stage II colorectal cancer (CRC) shows marked prognostic heterogeneity, yet existing single serum or imaging markers are insufficient for reliable high‑risk patient stratification. This study aimed to investigate the value of diffusion-weighted imaging (DWI), fluorine-18fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and serum carcinoembryonic antigen (CEA) for evaluating high‑risk status in stage II colorectal cancer. Both DWI and 18F‑FDG PET/CT could independently identify high‑risk patients, and their combination with CEA further improved predictive performance. This multimodal strategy integrating imaging and serum biomarkers offers a non‑invasive tool to assist individualized risk‑stratified clinical management for stage II colorectal cancer.</p><p><strong>Methods: </strong>A total of 106 patients with pathologically confirmed CRC were retrospectively enrolled and divided into high-risk stage II and non-high-risk stage II groups according to high-risk factors. Clinical data were collected, and apparent diffusion coefficient (ADC) values and maximum standardized uptake value (SUVmax) of lesions were measured in both groups. The consistency of each parameter was assessed using the intraclass correlation coefficient (ICC). Independent-samples <i>t</i>-test or Mann-Whitney U test was adopted for inter-group comparison of continuous variables. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic efficacy of each parameter, and the area under the curve (AUC) was calculated. The optimal cutoff value, corresponding sensitivity, and specificity were determined according to the Youden index. Binary logistic regression analysis was used to combine ADC value, SUVmax, and carcinoembryonic antigen (CEA). The Delong test was applied to compare AUC values between the combined model and single parameters. Correlations between ADC value and SUVmax were analyzed using Pearson correlation (for normally distributed data) or Spearman correlation (for non-normally distributed data).</p><p><strong>Results: </strong>ADC values were significantly lower in the high-risk stage II CRC group than in the non-high-risk group (P<0.05), while SUVmax and CEA levels were significantly higher (P<0.05). The combined model of ADC value, SUVmax, and CEA showed better diagnostic performance than any single parameter (P<0.05). A strong negative correlation between SUVmax and ADC value was observed in both the high-risk group r=-0.638 and the non-high-risk group r=-0.777.</p><p><strong>Conclusions: </strong>The combination of DWI, ¹⁸F-FDG PET/CT, and CEA is significantly superior to single modalities in evaluating the benefit of postoperative chemotherapy in CRC patients, and can provide clinical benefit for patients.</p>","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"235"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic significance of lymphovascular invasion in colorectal mucinous adenocarcinoma after curative resection: a nationwide multi-center retrospective cohort study.","authors":"Tianhao Wang, Bin Ren, Haoqing He, Peng Zhang, Changqing Jing, Xu Guan, Yueming Sun, Yifei Zhang, Shanglei Ning, Minhao Yu, Yanfeng Lv, Lei Wang, Meng Jiao, Yanlai Sun, Qi Sun, Dongning Liu, Xianghai Ren, Zhibin Ye, Zhao Zhang, Jun Li, Bin Ma, Guodong Yu, Wei Fu, Guangyong Zhang, Hui Yang","doi":"10.21037/jgo-2026-0275","DOIUrl":"10.21037/jgo-2026-0275","url":null,"abstract":"<p><strong>Background: </strong>Colorectal mucinous adenocarcinoma (MAC) shows heterogeneous outcomes after curative resection, and the prognostic value of lymphovascular invasion (LVI) remains unclear. This study evaluated the association of LVI with clinicopathological features, overall survival (OS), and disease-free survival (DFS) in patients with colorectal MAC.</p><p><strong>Methods: </strong>This retrospective cohort study included 2,391 adults with stage I-III pathologically confirmed colorectal MAC who underwent curative resection across 21 hospitals in China between 2016 and 2021. Patients were excluded if they had a mucinous component <50%, age <18 or >80 years, multiple primary tumors, synchronous multiple colorectal cancers, stage IV disease, or incomplete clinical, pathological, or follow-up data. Baseline clinicopathological factors included sex, age, tumor size, tumor location, differentiation, LVI status, T category, N category, and tumor-node-metastasis (TNM) stage. LVI was defined as tumor cells within endothelial-lined spaces or destruction of a lymphovascular wall. Postoperative surveillance was performed every 3 months for 2 years and every 6 months thereafter up to 5 years. OS and DFS were obtained from follow-up records and analyzed using Kaplan-Meier, log-rank, and Cox proportional hazards methods. LVI was fixed as the primary exposure in multivariable Cox models, and collinearity among candidate covariates was assessed using the variance inflation factor (VIF). All statistical tests were two-sided.</p><p><strong>Results: </strong>Among 2,391 patients, 1,397 (58.4%) were male, 994 (41.6%) were female, and 1,231 (51.5%) had stage III disease. LVI was detected in 541 patients (22.6%). LVI positivity was significantly associated with poor differentiation, advanced T stage, N stage, and TNM stage (all P<0.001). The 3-year OS rate was lower in the LVI-positive group than in the LVI-negative group (74.2% <i>vs.</i> 86.5%, P<0.001), as was the 3-year DFS rate (73.8% <i>vs.</i> 86.3%, P<0.001). In fixed-LVI multivariable Cox models, LVI positivity remained independently associated with poorer OS [hazard ratio (HR) =1.855; 95% confidence interval (CI): 1.319-2.607, P<0.001] and DFS (HR =1.923; 95% CI: 1.366-2.706, P<0.001). Subgroup analyses showed that LVI-positive patients had poorer OS and DFS than LVI-negative patients in left-sided colon, rectum, T3 stage, lymph node metastasis, and TNM stage III subgroups.</p><p><strong>Conclusions: </strong>In patients with MAC after curative resection, LVI was associated with more advanced pathological features and independently predicted poorer OS and DFS. Routine assessment of LVI may improve postoperative risk stratification and help identify patients who require closer surveillance.</p>","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"234"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sphingosine-1-phosphate induces angiogenesis via the activating STAT3 signaling pathway to drive colorectal cancer progression.","authors":"Qianlong Ling, Qidong Chen, Ruipeng Wang, Wenjiang Man, Liang Yan, Bowen Han, Zhongxu Yuan","doi":"10.21037/jgo-2026-0448","DOIUrl":"10.21037/jgo-2026-0448","url":null,"abstract":"<p><strong>Background: </strong>Tumor angiogenesis is a characteristic hallmark of carcinogenesis. Sphingosine-1-phosphate (S1P), a key bioactive lipid produced <i>in vivo</i>, drives the progression of multiple malignant tumors. This study aimed to investigate the function of S1P-mediated angiogenesis in the progression of colorectal cancer (CRC).</p><p><strong>Methods: </strong><i>CD31</i> expression was detected by immunohistochemistry, while serum levels of S1P and CD31 were measured by enzyme-linked immunosorbent assay (ELISA). The regulatory network linking S1P to <i>STAT3/VEGFA</i> was analyzed via the Search Tool for Interacting Chemicals (STITCH) database. Protein and messenger RNA levels were examined via Western blotting and quantitative real-time polymerase chain reaction. Cell Counting Kit-8 (CCK-8), colony formation, wound-healing, Transwell, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assays were applied to assess the phenotypic effects of S1P on CRC cells and human umbilical vein endothelial cells (HUVECs). A tube formation assay was conducted to assess angiogenesis <i>in vitro</i>, and a mouse xenograft model was created for <i>in vivo</i> studies.</p><p><strong>Results: </strong><i>CD31</i> and <i>VEGFA</i> were upregulated in CRC tissues. Serum S1P and CD31 concentrations were elevated and positively correlated with one another in patients with CRC. In terms of mechanism, S1P aggravated the malignant behaviors of CRC cells, activated the phosphorylated <i>STAT3-VEGFA</i> axis, and promoted tube formation in HUVECs. Knockdown of <i>VEGFA</i> significantly attenuated the S1P-induced promotion of malignant phenotypes in CRC cells. Inhibition of the <i>STAT3</i> pathway abolished the S1P-mediated enhancement of HUVEC phenotypes and angiogenesis. Mouse xenograft experiments verified that S1P facilitates tumor growth by activating the STAT3/VEGFA pathway.</p><p><strong>Conclusions: </strong>S1P facilitates the emergence of malignant phenotypes of CRC cells, the angiogenesis of HUVECs <i>in vitro</i>, and tumor growth <i>in vivo</i> via the modulation of the STAT3-VEGFA signaling axis. These findings suggest that S1P may be a valuable diagnostic and prognostic marker for CRC progression.</p>","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"227"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Screening of molecular biomarkers ASPN and LBH and construction of a prediction nomogram for the progression of esophagogastric junction adenocarcinoma.","authors":"Ye Chen, Liuhong Yuan, Huihui Sun, Ying Chen, Bo Li, Meng Qian, Zhenxiang Wang, Yan Zhang, Jie Xiong, Qian Liang, Kun Tao, Zhenyu Tan, Shuchang Xu","doi":"10.21037/jgo-2026-0361","DOIUrl":"10.21037/jgo-2026-0361","url":null,"abstract":"<p><strong>Background: </strong>Esophagogastric junction adenocarcinoma (EGJA) is an aggressive malignancy of the digestive system with poor prognosis. Early diagnosis and accurate prediction of tumor progression remain major clinical challenges. This study aimed to identify and validate molecular biomarkers and construct a precise diagnostic model, providing a scientific basis for individualized treatment.</p><p><strong>Methods: </strong>Differentially expressed genes (DEGs) associated with EGJA were identified using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed for further screening. The protein expression levels of ASPN and LBH were validated by immunohistochemistry in both tumor and adjacent non-tumor tissues. A nomogram was constructed by integrating clinical and pathological features, and its performance and clinical utility were assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).</p><p><strong>Results: </strong>Immunohistochemistry demonstrated that the protein expression of ASPN was significantly upregulated in tumor tissues, with expression levels increasing with tumor stage. Conversely, LBH was downregulated in tumor tissues and decreased with advancing stages. The predictive model achieved an area under the curve (AUC) value of 0.977, indicating excellent diagnostic and prognostic performance. DCA confirmed the clinical net benefit of the model.</p><p><strong>Conclusions: </strong>ASPN and LBH are critical molecular biomarkers for EGJA. The nomogram combining these two markers enables accurate distinction between early and advanced-stage tumors, offering significant support for early diagnosis of EGJA.</p>","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"212"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomic prediction of early progression at 2 years post-treatment in patients with resectable rectal cancer based on rectal tumor and mesentery characteristics: a two-center study.","authors":"Tao Quan, Lei Wu, Shuangqing Chen, Yuanqing Liu, Lu Lu, Jie Zhu","doi":"10.21037/jgo-2026-0451","DOIUrl":"10.21037/jgo-2026-0451","url":null,"abstract":"<p><strong>Background: </strong>Resectable rectal cancer carries a high risk of postoperative early progression (EP) within 2 years, which seriously affects patients' long-term survival outcomes. Current routine clinical evaluation strategies cannot accurately identify high-risk individuals, while radiomics and deep learning derived from preoperative magnetic resonance imaging (MRI) allow quantitative mining of hidden tumor microenvironmental features. This study aimed to explore the predictive value of preoperative MRI radiomics and deep learning for EP in patients with rectal cancer.</p><p><strong>Methods: </strong>A retrospective analysis included 255 patients with rectal cancer from The First Affiliated Hospital of Soochow University (Center 1) and 69 patients from the Suzhou Hospital Affiliated to Nanjing Medical University (Center 2). Patients from Center 1 were divided into a training set (n=204) and a test set (n=51) in an 8:2 ratio. Patients from Center 2 served as the external validation set (n=69). These patients were followed up, and the occurrence of EP (tumor recurrence or metastasis within 2 years of treatment) was recorded. Of the 324 patients with rectal cancer, 81 had EP and 243 did not. Features were extracted and filtered from MRI imaging data to construct radiomics and deep-radiomics models. Receiver operating characteristic (ROC) curves were plotted. The area under the ROC curve (AUC) was calculated to compare the models' predictive performance. Additionally, decision curves were used to assess the models' discriminatory performance. The SHapley Additive exPlanations (SHAP) method was used to visually demonstrate the feature contributions in the performance model and to illustrate the interpretability of the best model.</p><p><strong>Results: </strong>Nine radiomic features and ten deep radiomic features were selected. For the radiomics model, a multi-layer perceptron (MLP) was chosen as the classifier. For the deep radiomics model, extremely randomized trees (ExtraTrees) were chosen as the classifier. According to SHAP, the features contributing most markedly to the deep radiomics model were, in order: DL_resnet18_3, DL_resnet18_6, and T2surround_wavelet-HTL_glcm_Imc2. The deep-radiomics model [AUC =0.881; 95% confidence interval (CI): 0.821-0.940] significantly outperformed the radiomic model (AUC =0.777; 95% CI: 0.701-0.854). The Delong test revealed that the difference in predictive performance between the two models was extremely statistically significant (P<0.02).</p><p><strong>Conclusions: </strong>A deep-radiomics model based on pre-treatment MRI images demonstrates good performance in predicting post-treatment EP outcomes in patients with resectable rectal cancer. The SHAP algorithm provides interpretability at the individual level and supplies a basis for personalized post-treatment care in these patients.</p>","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"233"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing hepatocellular carcinoma screening in the era of metabolic dysfunction-associated steatotic liver disease and alcohol-related liver disease: time to lower the alpha-fetoprotein threshold.","authors":"Jennifer Au, Sang Hee K Choi, Jill Lane, Gauree G Konijeti","doi":"10.21037/jgo-2026-0712","DOIUrl":"10.21037/jgo-2026-0712","url":null,"abstract":"","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"275"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and preliminary validation of plasma cell-free DNA methylation-based diagnostic prediction model for colorectal cancer detection.","authors":"Ying Qu, Runheng Huang, Yaqing Chen, Peng Xia, Bin Huang, Jie Liu, Lingxiang Wu, Ruohan Zhang, Qianghu Wang","doi":"10.21037/jgo-2026-0689","DOIUrl":"10.21037/jgo-2026-0689","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Colorectal cancer (CRC) is a common malignancy associated with genetic and epigenetic alterations. Several methylation biomarkers have been investigated for non-invasive CRC detection; however, their reported performance varies across clinical settings, and the detection of early-stage or precancerous disease and discrimination from non-malignant colorectal conditions remain challenging. This exploratory study aimed to identify reproducible CRC-associated plasma cell-free DNA (cfDNA) methylation regions and to develop and preliminarily evaluate diagnostic prediction model for distinguishing CRC from healthy controls and benign samples.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Public CRC tissue methylation datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify reproducible CRC-associated methylation alterations. Plasma cfDNA methylation was profiled using methyl-CpG-binding-domain enrichment followed by paired-end sequencing in patients with CRC, patients with colorectal polyps, and healthy controls. After quality-control filtering, 30 CRC and healthy-control samples were randomly allocated at the participant level in a 7:3 ratio to a development set comprising 10 patients with CRC and 11 healthy controls and a held-out test set comprising 4 patients with CRC and 5 healthy controls. Hypermethylated regions were selected using least absolute shrinkage and selection operator (LASSO) logistic regression. The 12-region model was evaluated in the held-out test set and subsequently applied to 10 colorectal polyp samples without refitting or recalibration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Tissue methylation analysis identified reproducible CRC-associated alterations across independent datasets. In the plasma development set, 707 differentially methylated regions (DMRs) were identified between CRC and healthy-control samples, including 324 hypermethylated and 383 hypomethylated regions. LASSO regression selected a 12-region hypermethylation signature. In the held-out test set, the model achieved an area under the curve (AUC) of 0.85 [95% confidence interval (CI): 0.579-1.000]. At the development-set-derived threshold, sensitivity was 75.0% (3/4), specificity was 60.0% (3/5), and accuracy was 66.7% (6/9). When the original model was applied to colorectal polyp samples, model scores were significantly higher in both CRC and polyp samples than in healthy controls, while CRC samples showed a tendency toward higher scores than polyp samples.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This exploratory study identified a 12-region plasma cfDNA hypermethylation signature associated with CRC and developed a LASSO-based diagnostic prediction model that showed preliminary discrimination between CRC and healthy controls in a small held-out test set. By integrating tissue methylation evidence with plasma cfDNA profiling, this study expands the repertoire of candidate region-level methylation markers for ","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"244"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive prediction of occult peritoneal metastasis (OPM) in gastric cancer using logistic regression and random forest integrative models with CT radiomics and clinical parameters: machine learning prediction of gastric OPM.","authors":"Yuzhe Han, Shuai Xiang, Hanhui Jing, Hongyu Cao, Ying Li, Jiazhen Sui, Jinlong Deng, Ning Ding, Shanglong Liu","doi":"10.21037/jgo-2026-0509","DOIUrl":"10.21037/jgo-2026-0509","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Occult peritoneal metastasis (OPM) in gastric cancer is frequently missed on preoperative contrast-enhanced computed tomography (CT), leading to understaging in about 30-40% of patients and necessitating invasive laparoscopy for confirmation. A non-invasive tool to preoperatively identify OPM would help avoid unnecessary surgery and guide individualized treatment, but existing models lack interpretability and do not compare linear &lt;i&gt;vs.&lt;/i&gt; nonlinear decision mechanisms. Therefore, we aimed to develop interpretable integrated models and compare their decision mechanisms and predictive performance for non‑invasive preoperative prediction of gastric OPM.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We retrospectively enrolled 164 gastric adenocarcinoma patients (100 with OPM, 64 non-metastatic) from a single center. Inclusion criteria were biopsy-proven adenocarcinoma, no prior malignancy, complete clinical data, and contrast-enhanced CT at initial diagnosis showing no definite peritoneal metastasis, with OPM subsequently confirmed by laparoscopic exploration, peritoneal biopsy, or positive peritoneal lavage cytology. Exclusion criteria included prior treatment, poor-quality images, unconfirmed peritoneal lesions, other distant metastases, or remnant stomach. Nine clinical-laboratory variables [age, sex, body mass index, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), albumin (ALB), tumor location, and tumor/node (T/N) stage] were collected, and three-dimensional (3D) radiomics features were extracted from primary-tumor CT volumes. The cohort was randomly split 7:3 into training (n=114, 70 OPM) and validation (n=50, 30 OPM) sets using stratified sampling. After intraclass correlation coefficient (ICC) (&gt;0.75) filtering, least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation [1 - standard error (1-SE) rule] and Spearman correlation (threshold 0.75) selected six stable radiomics features from CT images. Integrated models combining age, T stage, and radiomics signature were built using five machine learning algorithms. Evaluation included area under the curve (AUC), calibration curves, Brier score, decision curve analysis (DCA), precision-recall (PR) curves, and average precision (AP). SHapley Additive exPlanations (SHAP) analysis was performed on both logistic regression (LR) and random forest (RF) models, and Friedman's H-statistic quantified feature interactions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Age and T stage were independent clinical predictors. The integrated LR model achieved training/validation AUCs of 0.907/0.868, with validation sensitivity of 0.833 and specificity of 0.800. The RF model yielded training/validation AUCs of 0.839/0.857, with validation sensitivity of 0.833 and specificity of 0.800. SHAP comparison revealed crossing decision paths in LR &lt;i&gt;vs.&lt;/i&gt; cleaner boundaries in RF, with moderate feature interactions (H=0.100-0.136). A simpli","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"217"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel glutamine metabolism-based classification system for characterizing the heterogeneity of hepatocellular carcinoma.","authors":"Meijun Li, Zhiyi Chen, Xudong Shen, Zhenyu Feng, Shaohua Wei, Yecheng Li, Minjian Zhang, Zhenyu Ye","doi":"10.21037/jgo-2026-0706","DOIUrl":"10.21037/jgo-2026-0706","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Glutamine dependence is a hallmark of tumor cell metabolism, and further molecular classification based on glutamine metabolism in patients with hepatocellular carcinoma (HCC) may provide clinical value. This study thus comprehensively examined the patterns of HCC-specific alterations in glutamine metabolism.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Consensus clustering analysis was conducted on samples from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset based on glutamine metabolism-related genes, which was validated in the GSE76427, the Liver Cancer-France (LICA-FR) cohort, and the Liver Cancer-Japan (LIRI-JP) cohort from the ICGC. Somatic mutation features were evaluated with the Maftools package in R. The activity of oncogenic pathways was estimated via gene set enrichment analysis (GSEA) or single-sample GSEA (ssGSEA). The tumor microenvironment was analyzed using both the CIBERSORT algorithm (for immune cell infiltration estimation) and the ESTIMATE algorithm (for stromal and immune score calculation). Drug sensitivity and immune checkpoint blockade (ICB) response were also analyzed, for which a classifier was built via least absolute shrinkage and selection operator (LASSO). Immunohistochemistry (IHC) was performed to validate the protein expression levels of key differentially expressed genes (DEGs). Intracellular glutamine content under different glutamine concentrations was measured. The viability of HCC cell lines under varying glutamine concentrations was assessed via Cell Counting Kit-8 (CCK-8) assays. Cell migration and invasion were evaluated through Transwell assays, and protein expression was analyzed via Western blotting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;HCC samples were classified into two glutamine metabolism-based clusters, with cluster 1 having a more advanced stage of disease and shorter survival than cluster 2. A higher frequency of genetic mutations and stronger activation of oncogenic pathways was found in cluster 1. There were substantial differences in immune cell infiltration and stromal scores between clusters 1 and 2. Cluster 1 exhibited significantly higher infiltration of immunosuppressive cells and lower stromal scores compared to cluster 2. Cluster 1 had a stronger response to ICB due as indicated by a higher tumor mutation burden (TMB) and T cell-inflamed gene expression profile score, immune checkpoints, and Tumor Immune Dysfunction and Exclusion (TIDE)-predicted data. Moreover, the LASSO classifier accurately differentiated the two clusters. The DEGs between the two clusters were validated in clinical samples. IHC confirmed the differential expression of glutamine metabolism-related genes in HCC samples. CCK-8 assays showed no significant effect of glutamine concentration on cell proliferation. However, Transwell assays revealed that glutamine deprivation (0.2 mM) reduced migration and invasion, while high-glutamine conditions (10 mM) promoted them. Western bl","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"258"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of spatial distribution of M2 macrophages on prognosis and neoadjuvant chemotherapy resistance in gastric cancer.","authors":"Yangquan Li, Can Luo, Dongfang Li","doi":"10.21037/jgo-2026-0398","DOIUrl":"10.21037/jgo-2026-0398","url":null,"abstract":"<p><strong>Background: </strong>Neoadjuvant chemotherapy (NAC) is a crucial treatment for locally advanced gastric cancer; however, approximately 30-40% of patients experience primary resistance, the mechanisms of which urgently require elucidation. The tumor microenvironment exhibits a high degree of spatial heterogeneity. M2 macrophages, as critical immune cells within this environment, are typically associated with poor prognosis. Yet, whether their spatial distribution impacts chemotherapy efficacy remains unclear. This study aims to investigate the relationship between the <i>in situ</i> spatial distribution characteristics of M2 macrophages and chemoresistance in gastric cancer.</p><p><strong>Methods: </strong>Based on The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort, the association between M2 markers (<i>CD163</i>, <i>MRC1</i>) and histological grade as well as overall survival (OS) was evaluated. Spearman correlation and functional enrichment analyses were conducted to explore the mechanistic link between M2 macrophages and stromal barrier construction. Multiplex immunofluorescence (mIF) and digital pathology image analysis were utilized to calculate the areal density of M2 macrophages in the intratumoral core and the peritumoral stroma, respectively. The tumor-to-peritumoral ratio (TPR) was constructed, followed by a rank correlation analysis between TPR and the tumor regression grade (TRG).</p><p><strong>Results: </strong>TCGA-STAD results confirmed that patients with high expression of M2 markers had worse OS (P=0.03), and the expression levels of M2 markers increased with histological grade. <i>MRC1</i> was highly significantly and positively correlated with the pro-fibrotic factor <i>TGFB1</i> (rho=0.447, P<0.001), with the gene set significantly enriched in pathways such as positive regulation of cytokine production and myeloid leukocyte activation. Histological examination revealed that in chemoresistant patients (TRG 3), M2 macrophages were primarily retained in the peritumoral stroma, with a median TPR of 0.50; in chemosensitive patients (TRG 1-2), a massive influx of M2 macrophages into the tumor core was observed, with a median TPR of 6.67. TPR was negatively correlated with TRG (r_s=-0.65, P=0.043).</p><p><strong>Conclusions: </strong>The clinical impact of M2 macrophages in the gastric cancer microenvironment is highly dependent on their spatial distribution. The peritumoral-enriched pattern (TPR <1) mediates primary chemoresistance, whereas high infiltration in the core objectively reflects the pathological footprint following effective chemotherapy. The TPR serves as a novel tool for assessing neoadjuvant chemosensitivity in gastric cancer.</p>","PeriodicalId":15841,"journal":{"name":"Journal of gastrointestinal oncology","volume":"17 4","pages":"221"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}