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Histologic subtype, dNLR, AMC, and IL-6 emerged as independent prognostic factors, supporting responder stratification despite restricted long-term 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Treated With Anti‐PD‐1 Therapy  \nChunxia Feng1 | Ping Li1  | Qianhui Gu1 | Minbin Chen1 | Jinhua Gu2  \n1Department of Radiotherapy and Oncology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, Jiangsu, China | 2Department of Clinical Laboratory, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, Jiangsu, China  \nCorrespondence: Jinhua Gu ([tulip1981@163.com](tulip1981@163.com))  \nReceived: 7 June 2025 | Revised: 23 February 2026 | Accepted: 27 February 2026  \nFunding: Suzhou Science and Technology Plan Project, Grant/Award Numbers: SKY2023093, LCZX202228; Science and Technology Project of Suzhou Municipal Health Commission, Grant/Award Number: MSXM2024060; Kunshan Science and Technology Plan Project, Grant/Award Number: KS2503 Keywords: advanced cancer | PD‐1 inhibitors | peripheral blood markers | prognostic biomarkers | treatment response prediction  \nABSTRACT  \nObjective: PD‐1 inhibitors are increasingly used in advanced cancers, but reliable biomarkers for predicting treatment response remain limited.  \nMethods: In this retrospective cohort study, we analyzed 335 patients with stage IV cancers treated with anti‐PD‐1 therapy (The First People's Hospital of Kunshan, 2019–2025) . A predictive model integrating peripheral blood markers and clinical factors was developed using logistic and Cox regression.  \nResults: Histologic subtype, dNLR ≤ 3.18, AMC ≤ 0.48, and IL‐6 ≤ 12.35 were independent prognostic factors for OS and PFS (multivariate HRs reported) . Patients with ≥ 3 favorable factors had significantly better outcomes. The model showed high accuracy for 1‐year survival prediction (AUC = 0.83) but limited performance beyond 3 years.  \nConclusion: This study proposes a clinically feasible biomarker‐based tool to stratify PD‐1 inhibitor responders, though further validation in prospective cohorts is needed.  \n1 | Introduction  \nThe management of advanced malignant tumors poses a substantial global public health challenge because of their high mortality and limited curative options [ 1] . In China, the incidence and mortality of advanced cancers such as lung, liver, and pancreatic cancers have increased considerably, posing a severe threat to public health [2] . Advancements in medical technology have introduced immunotherapies, such as programmed cell death‐1 (PD‐1) inhibitors, to patients with advanced cancers by leveraging their immune response against tumors [ 3, 4] . Integrating PD‐1 inhibitors into standard treatment protocols has emerged as a vital component for the treatment of refractory tumors, highlighting their growing importance in clinical practice [ 5] .  \nPD‐1 inhibitors enhance the ability of T cells to destroy tumor cells by blocking the PD‐1/Programmed death‐ligand 1 (PD‐L1) signaling pathway, thereby counteracting tumor‐induced immune suppression [4, 5] . Researchers have demonstrated the efficacy of PD‐1 inhibitors for the treatment of advanced malignancies [5, 6] . Clinical trials such as Keynote‐001 [7], Keynote‐042 [8], and CheckMate‐227 [9] have confirmed their efficacy in treating advanced malignancies. However, responses to PD‐1 inhibitors vary considerably, with some patients demonstrating suboptimal responses or developing hyperprogressive disease [5] . Therefore, predicting treatment response to PD‐1 inhibitor therapy and identifying patients most likely to benefit from treatment is critical. Biomarkers in tumor tissues, such as PD‐L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI), have been extensively  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2026 The Author(s) . Immunity, Inflammation and Disease published by John Wiley & Sons Ltd.  \nImmunity, Inflammation","cbCaitcNqH3MZ5Bo","https://ap.wps.com/l/cbCaitcNqH3MZ5Bo","pdf",1457362,12,"English","# Introduction\n## PD-1 inhibitors and immune mechanisms\n## Need for reliable predictive biomarkers\n# Methods\n## Study design and patient cohort\n## Predictive model development","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To identify and evaluate peripheral blood biomarkers that can predict outcomes in advanced cancers treated with anti-PD-1 therapy.\"},{\"question\":\"How was the predictive model developed?\",\"answer\":\"A retrospective cohort of 335 stage IV patients was analyzed, and a model integrating peripheral blood markers and clinical factors was built using logistic and Cox regression.\"},{\"question\":\"Which peripheral blood-related factors were independent prognostic predictors?\",\"answer\":\"Histologic subtype, dNLR (≤ 3.18), AMC (≤ 0.48), and IL-6 (≤ 12.35) were reported as independent prognostic factors for OS and PFS.\"}]","Peripheral Blood Biomarkers Predict Outcomes in Advanced Cancers Treated With Anti-PD-1 Therapy | PDF",1790109123]