Evidence map›Paper›PMID 40682474›Full record

ArticleJournal of cellular and molecular medicine2025

Inflammatory Pathways and Immune Microenvironment in Non-Small Cell Lung Cancer: Multi-Dimensional Analysis and Machine Learning Prediction.

Yuan Fang, Yuli Wang, Lanlan Yang, Si Yuan, Jiefei Gu, Ziyi Zhou, Zhihong Fang, Yan Li

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Yuan FangClinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.ORCID 0009-0005-3581-4455
Yuli WangClinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Lanlan YangClinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Si YuanDepartment of Oncology, Nujiangzhou Hospital of Traditional Chinese Medicine, Yunnan, China.
Jiefei GuInformation Center, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Ziyi ZhouClinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Zhihong FangClinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yan LiClinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Funding

National Natural Science Foundation of China 82174183National Natural Science Foundation of China 82204842Scientific Research Program of Shanghai Municipal Science and Technology Commission 24010703100Shanghai Famous Traditional Chinese Medicine Expert Studio SHGZS-202210
6 · The paper itself

Abstract

Non-small cell lung cancer (NSCLC) is a highly complex malignancy involving multiple molecular pathways including inflammatory responses, immune regulation and cell cycle dysregulation. Although previous studies have indicated the important role of inflammatory factors in NSCLC pathogenesis, the causal relationship between specific inflammatory factors and NSCLC risk, as well as their interactions with the immune microenvironment, has not been comprehensively elucidated. This study systematically evaluated the causal relationship between various inflammatory factors and NSCLC risk using Mendelian randomisation (MR) methodology. Through comprehensive transcriptomic analysis, network pharmacology approaches and protein-protein interaction network construction, we revealed molecular targets and key pathways in NSCLC. Additionally, we applied machine learning models to predict NSCLC and analysed the correlation between immune cell composition and cell cycle regulatory genes in NSCLC using flow cytometry. MR analysis showed that TGFB1 and CCL11 were positively correlated with NSCLC risk (OR = 1.173, p = 0.020; OR = 1.192, p = 0.003), while CD40 and CCL4 demonstrated protective effects (OR = 0.857, p = 0.015; OR = 0.896, p = 0.049). Bioinformatic analysis identified 74 overlapping drug-disease targets enriched in multiple inflammation-related signalling pathways. Machine learning models performed well in predicting NSCLC with AUC values of 0.723-0.763. Immune cell analysis revealed significantly increased CD8+ T cells and regulatory T cells (Tregs) in NSCLC samples, while naïve B cells were decreased. Complex correlations existed between cell cycle regulatory genes and immune cell composition, with CDK2 and CDK3 negatively correlated with Tregs (R = -0.8, p = 0.014; R = -0.72, p = 0.037), while CDK5 positively correlated with Tregs (R = 0.8, p = 0.014). This study revealed genetic associations between specific inflammatory factors and NSCLC risk, elucidating the complex interactions between inflammatory pathways and the immune microenvironment in NSCLC pathogenesis.

Indexed as

Carcinoma, Non-Small-Cell LungInflammationLung NeoplasmsMachine LearningTumor MicroenvironmentComputational BiologyGene Expression Regulation, NeoplasticHumansProtein Interaction MapsSignal Transductionbiomarkerscell cycleimmune microenvironmentinflammatory factorsmachine learningMendelian randomisationnon‐small cell lung cancer

Identifiers

PMID40682474
PMCPMC12274958

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.