Evidence map›Paper›PMID 40958865›Full record

ArticleFrontiers in oncology2025

The application of peripheral blood immune profiling in personalized treatment of locally advanced and advanced lung cancer: a nomogram approach.

Tianwen Yin, Yikun Li, Qixin Sun, Qipeng Yuan, Shan Zhu, Jinming Yu, Tao Zhang, Feifei Teng, Chuanwang Miao

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tianwen YinCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yikun LiDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Qixin SunSchool of Clinical Medicine, Shandong Second Medical University, Weifang, China.
Qipeng YuanDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Shan ZhuDepartment of Radiation Oncology, Shandong Provincial Otorhinolaryngology (ENT) Hospital, Shandong University, Jinan, Shandong, China.
Jinming YuCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Tao ZhangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Feifei TengDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Chuanwang MiaoDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Immunotherapy has revolutionized the treatment of lung cancer, yet many patients experience limited or transient benefits. Identifying those most likely to benefit remains a critical challenge. This study aims to establish a predictive model based on peripheral blood lymphocyte subsets to evaluate treatment responses in locally advanced and advanced lung cancer patients receiving chemotherapy with or without immunotherapy. Methods: We prospectively enrolled 171 patients, peripheral blood lymphocyte subsets were analyzed pre-treatment, post-treatment, and at disease progression using flow cytometry, focusing on CD3 Results: Baseline lymphocyte profiles were closely associated with treatment responses. Elevated CD3 Conclusion: Peripheral blood lymphocyte subsets are promising non-invasive biomarkers for predicting treatment responses in locally advanced and advanced lung cancer patients, particularly with immunotherapy. The developed nomogram models enhance predictive accuracy, supporting personalized treatment decisions.

Indexed as

biomarkerimmunotherapylung cancerlymphocyte subsetsnomogram

Identifiers

PMID40958865
PMCPMC12433846

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.