Evidence map›Paper›PMID 41358202›Full record

ArticleFrontiers in molecular biosciences2025

Prognostic model for lung adenocarcinoma based on experimental drug-resistant cell lines and clinical patients.

Junnan Li, Jiasheng Zhang, Xinyang Zhang, Chengwu Cao, Tianjie Zhou, Fengxian Liu, Liqing Hu, Hang Fai Kwok, Hui Zou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Junnan Li *Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Pharmacy, Health Science Center, Hunan Normal University, Changsha, China.
Jiasheng Zhang *Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macao SAR, China.
Xinyang ZhangKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Pharmacy, Health Science Center, Hunan Normal University, Changsha, China.
Chengwu CaoKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Pharmacy, Health Science Center, Hunan Normal University, Changsha, China.
Tianjie ZhouKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Pharmacy, Health Science Center, Hunan Normal University, Changsha, China.
Fengxian LiuKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Pharmacy, Health Science Center, Hunan Normal University, Changsha, China.
Liqing HuKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Pharmacy, Health Science Center, Hunan Normal University, Changsha, China.
Hang Fai KwokDepartment of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macao SAR, China.
Hui ZouKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Pharmacy, Health Science Center, Hunan Normal University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Despite advances in EGFR-TKIs for lung adenocarcinoma (LUAD), resistance remains a major hurdle. This study aimed to develop a prognostic model integrating immune microenvironment features and Materials and methods: erlotinib-, gefitinib-, and osimertinib-resistant HCC827 cell lines were established by exposing them to increasing EGFR-TKIs concentrations. RNA-sequencing was conducted on non-resistant HCC827 and erlotinib/gefitinibresistant cell lines. From the erlotinib-resistant, gefitinib-resistant cell lines and The Cancer Genome Atlas Program-Lung adenocarcinoma (TCGA-LUAD) data, a prognostic risk score model was constructed via Least Absolute Shrinkage and Selection Operator-Cox Proportional Hazards Model (LASSO-COX). Furthermore, immune infiltration was assessed using Gene Set Variation Analysis (GSVA), and single-cell RNA-seq (GSE241934) resolved expression patterns in EGFR-mutant vs. wild-type tumors. Results: A 3-gene ( Conclusion: This model bridges

Indexed as

EGFR-TKIs resistanceimmune microenvironmentlung cancerprognosis predictionsingle cell RNA-seq (scRNA-seq)

Identifiers

PMID41358202
PMCPMC12678131

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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.