{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T21:43:59Z","timestamp":1792705439108,"version":"4.5.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Major pathological response (MPR) to neoadjuvant PD-1 inhibitor plus chemotherapy in locally advanced gastric cancer (LAGC) varies widely and is incompletely explained by tumor-side biomarkers. Because checkpoint inhibitors act within a host environment shaped by endocrine, nutritional, and inflammatory state, we developed and validated the Endocrine\u2013Nutritional Immunotherapy response score (ENI-Score) from routine pretreatment peripheral-blood markers to predict short-term response and early recurrence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 738 patients with LAGC or gastroesophageal junction adenocarcinoma who received neoadjuvant PD-1 inhibitor plus platinum-based chemotherapy and radical gastrectomy at four hospitals, partitioned into training (n=335), internal (n=114), and external (n=289) validation cohorts. Ten machine-learning algorithms were benchmarked, and a prespecified, domain-balanced five-marker panel comprising 8 AM cortisol, prognostic nutritional index (PNI), prealbumin, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein-to-albumin ratio (CAR) was carried forward in a regularized logistic model and rescaled to the ENI-Score (0 -100). Analyses included SHAP, tertile stratification, nested AUC comparison (DeLong), calibration, decision-curve analysis, and Kaplan\u2013Meier survival analysis; the primary endpoint was MPR (residual viable tumor \u226410%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The overall MPR rate was 40.9% (302\/738). All five markers differed significantly between MPR and non-MPR patients in a biologically coherent direction consistent across all three cohorts (each P&amp;lt;0.001). Regularized logistic regression achieved the highest, most stable external-validation AUC among the ten algorithms (training 0.873, internal 0.816, external 0.830, and five-fold cross-validated 0.862), whereas tree-based ensembles overfit (training AUC up to 0.99, external \u22640.81). ENI-Score tertiles separated MPR rates steeply (Low 10.4%, Intermediate 38.2%, High 79.1%; P-trend&amp;lt;0.001), with an adjusted High-benefit versus Low-benefit odds ratio of 35.3 (95% CI 20.6-60.5) and a per 10-point adjusted OR of 1.72 (95% CI 1.59-1.86, P&amp;lt;0.001). The ENI-Score reached an overall AUC of 0.842 and significantly augmented clinical staging (0.656 to 0.864; DeLong P&amp;lt;0.001). Higher ENI-Score was associated with longer recurrence-free survival (RFS; log-rank P&amp;lt;0.001; adjusted HR per 10 points 0.78).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Built entirely from five low-cost routine peripheral-blood markers, the ENI-Score accurately and reproducibly stratifies MPR after neoadjuvant immunochemotherapy in LAGC, augments tumor-centered clinical staging, and is associated with RFS. It provides an inexpensive, interpretable host-state adjunct to tumor-side biomarkers that warrants prospective validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1907116","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:55:15Z","timestamp":1786514115000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Peripheral endocrine\u2013nutritional machine learning signature predicts short-term response to neoadjuvant immunochemotherapy in locally advanced gastric cancer"],"prefix":"10.3389","volume":"17","author":[{"given":"Fang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Pathology, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zefan","family":"Mu","sequence":"additional","affiliation":[{"name":"The Third Department of Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]},{"name":"Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer","place":["Shijiazhuang, China"]},{"name":"Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghai","family":"Guo","sequence":"additional","affiliation":[{"name":"The Third Department of Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]},{"name":"Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer","place":["Shijiazhuang, China"]},{"name":"Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zheng","sequence":"additional","affiliation":[{"name":"The Third Department of Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]},{"name":"Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer","place":["Shijiazhuang, China"]},{"name":"Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peigang","family":"Yang","sequence":"additional","affiliation":[{"name":"The Third Department of Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]},{"name":"Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer","place":["Shijiazhuang, China"]},{"name":"Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Shijiazhuang People\u2019s Hospital","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery, The Fifth Affiliated Hospital of Anhui Medical University","place":["Hefei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Medical Record, The First College of Clinical Medical Science, China Three Gorges University and Yichang Central People\u2019s Hospital","place":["Yichang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"The Third Department of Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]},{"name":"Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer","place":["Shijiazhuang, China"]},{"name":"Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqian","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Endoscopy, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Third Department of Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]},{"name":"Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer","place":["Shijiazhuang, China"]},{"name":"Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jncc.2024.01.006","article-title":"Cancer incidence and mortality in China, 2022","volume":"4","author":"Han","year":"2024","journal-title":"J Natl Cancer Cent"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)31288-5","article-title":"Gastric cancer","volume":"396","author":"Smyth","year":"2020","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2022.0008","article-title":"Gastric cancer, version 2.2022, NCCN clinical practice guidelines in oncology","volume":"20","author":"Ajani","year":"2022","journal-title":"J Natl Compr Canc Netw"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2022.07.004","article-title":"Gastric cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up","volume":"33","author":"Lordick","year":"2022","journal-title":"Ann Oncol"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32557-1","article-title":"Perioperative chemotherapy with fluorouracil plus leucovorin, oxaliplatin, and docetaxel versus fluorouracil or capecitabine plus cisplatin and epirubicin for locally advanced, resectable gastric or gastro-oesophageal junction adenocarcinoma (FLOT4): a randomised, phase 2\/3 trial","volume":"393","author":"Al-Batran","year":"2019","journal-title":"Lancet"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(21)00297-7","article-title":"Perioperative or postoperative adjuvant oxaliplatin with S-1 versus adjuvant oxaliplatin with capecitabine in patients with locally advanced gastric or gastro-oesophageal junction adenocarcinoma undergoing D2 gastrectomy (RESOLVE): an open-label, superiority and non-inferiority, phase 3 randomised controlled trial","volume":"22","author":"Zhang","year":"2021","journal-title":"Lancet Oncol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/S0140-6736(21)00797-2","article-title":"First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial","volume":"398","author":"Janjigian","year":"2021","journal-title":"Lancet"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.19918","article-title":"Sintilimab plus chemotherapy for unresectable gastric or gastroesophageal junction cancer: the ORIENT-16 randomized clinical trial","volume":"330","author":"Xu","year":"2023","journal-title":"JAMA"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(23)00515-6","article-title":"Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for HER2-negative advanced gastric cancer (KEYNOTE-859): a multicentre, randomised, double-blind, phase 3 trial","volume":"24","author":"Rha","year":"2023","journal-title":"Lancet Oncol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(23)00541-7","article-title":"Neoadjuvant and adjuvant pembrolizumab plus chemotherapy in locally advanced gastric or gastro-oesophageal cancer (KEYNOTE-585): an interim analysis of the multicentre, double-blind, randomised phase 3 study","volume":"25","author":"Shitara","year":"2024","journal-title":"Lancet Oncol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2503701","article-title":"Perioperative durvalumab in gastric and gastroesophageal junction cancer (MATTERHORN)","volume":"393","author":"Janjigian","year":"2025","journal-title":"N Engl J Med"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.19.01124","article-title":"Individual patient data meta-analysis of the value of microsatellite instability as a biomarker in gastric cancer","volume":"37","author":"Pietrantonio","year":"2019","journal-title":"J Clin Oncol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0101-z","article-title":"Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer","volume":"24","author":"Kim","year":"2018","journal-title":"Nat Med"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/nm.3850","article-title":"Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes","volume":"21","author":"Cristescu","year":"2015","journal-title":"Nat Med"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.immuni.2020.08.005","article-title":"Endogenous glucocorticoid signaling regulates CD8+ T cell differentiation and development of dysfunction in the tumor microenvironment","volume":"53","author":"Acharya","year":"2020","journal-title":"Immunity"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1038\/nri.2016.147","article-title":"Immune regulation by glucocorticoids","volume":"17","author":"Cain","year":"2017","journal-title":"Nat Rev Immunol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"7041","DOI":"10.1038\/s41467-021-27349-7","article-title":"Glucocorticoid receptor regulates PD-L1 and MHC-I in pancreatic cancer cells to promote immune evasion and immunotherapy resistance","volume":"12","author":"Deng","year":"2021","journal-title":"Nat Commun"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14215297","article-title":"Focus on the dynamics of neutrophil-to-lymphocyte ratio in cancer patients treated with immune checkpoint inhibitors: A meta-analysis and systematic review","volume":"14","author":"Guo","year":"2022","journal-title":"Cancers (Basel)"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000031113","article-title":"Pretreatment prognostic nutritional index as an indicator of outcome in lung cancer patients receiving immune checkpoint inhibitor-based treatment: a systematic review and meta-analysis","volume":"101","author":"Shao","year":"2022","journal-title":"Med (Baltimore)"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1148786","article-title":"Prognostic role of C-reactive protein to albumin ratio in cancer patients treated with immune checkpoint inhibitors: a meta-analysis","volume":"13","author":"Dai","year":"2023","journal-title":"Front Oncol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00040-1","article-title":"Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study","volume":"4","author":"Jiang","year":"2022","journal-title":"Lancet Digit Health"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06368-5","article-title":"Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer","volume":"30","author":"Liu","year":"2020","journal-title":"Eur Radiol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"S0923","DOI":"10.1016\/j.annonc.2026.07.004","article-title":"A multimodal deep learning model for preoperative prediction of postoperative complications in gastric cancer","author":"Ding","year":"2026","journal-title":"Ann Oncol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.638362","article-title":"Computed tomography-based radiomics nomogram: potential to predict local recurrence of gastric cancer after radical resection","volume":"11","author":"Huang","year":"2021","journal-title":"Front Oncol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1097\/JS9.0000000000001627","article-title":"Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study","volume":"110","author":"Cao","year":"2024","journal-title":"Int J Surg"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-026-71347-6","article-title":"A deep learning-based digital biopsy for predicting early recurrence in gastric cancer","volume":"17","author":"Ding","year":"2026","journal-title":"Nat Commun"},{"key":"B27","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1038\/s41698-025-00875-z","article-title":"Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancer","volume":"9","author":"Ding","year":"2025","journal-title":"NPJ Precis Oncol"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202406276","article-title":"Transcriptomics-based liquid biopsy for early detection of recurrence in locally advanced gastric cancer","volume":"11","author":"Ding","year":"2024","journal-title":"Adv Sci (Weinh)"},{"key":"B29","article-title":"Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients","volume":"85","author":"Onodera","year":"1984","journal-title":"Nihon Geka Gakkai Zasshi"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.11660","article-title":"Histomorphology and grading of regression in gastric carcinoma treated with neoadjuvant chemotherapy","volume":"98","author":"Becker","year":"2003","journal-title":"Cancer"},{"key":"B31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"B33","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"B34","article-title":"LightGBM: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B35","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1907116\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:55:16Z","timestamp":1786514116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1907116\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":37,"alternative-id":["10.3389\/fimmu.2026.1907116"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2026.1907116","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]},"article-number":"1907116"}}