Evidence map›Paper›PMID 41251847›Full record

ArticleDiscover oncology2025

TKI resistance related genes in LUAD predict patient prognosis and tumor immune microenvironment with LHX2 as a hub gene.

Ruohan Huang, Qi Liang, Hongya Wang, Huizhu Qian, Renhua Guo

Abstract read
In one paragraph

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

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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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

5 authors.

Ruohan Huang *Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
Qi Liang *Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
Hongya WangDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Huizhu QianDepartment of Oncology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, 210011, China. qhznjmu@163.com.
Renhua GuoDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China. njmurhguo@163.com.

Funding

Postgraduate Research & Practice Innovation Program of Jiangsu Province SJCX24_0776the National Natural Science Foundation of China 82272669
6 · The paper itself

Abstract

backgroundNon-small cell lung cancer (NSCLC), particularly lung adenocarcinoma (LUAD), is a leading cause of cancer-related mortality. Osimertinib, a third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), improves survival, but acquired resistance remains a major clinical challenge.

methodsTranscriptomic profiles of osimertinib-sensitive and -resistant EGFR-mutant cell lines were analyzed to identify resistance mechanisms. Resistance-associated genes were used to develop a Cox proportional hazards (CoxPH) prognostic model based on The Cancer Genome Atlas (TCGA) data. Immune context and tumor mutational burden (TMB) were compared between risk groups. Core gene expression was validated by reverse transcription quantitative polymerase chain reaction (RT‒qPCR). Functional experiments including Cell Counting Kit-8 (CCK8) and methylthiazolyldiphenyl-tetrazolium bromide (MTT) assays were performed to evaluate the impact of core gene on the proliferative capacities of EGFR-TKI resistance cells.

resultsResistant cells showed gene expression alterations enriched in proliferation (e.g., Wnt) and cell cycle pathways. An 18-gene signature strongly predicted overall survival (concordance index [C-index] = 0.74). High-risk patients exhibited more immunosuppressive microenvironments and higher TMB. Nine genes (GLI2, LHX2, PITX3, MSX1, ANO1, MUC5B, BEST4, SLC46A3, ECEL1P2) were independent prognostic factors, and LHX2 occupied a central position. LHX2 was upregulated in resistant cells, and its knockdown restored osimertinib sensitivity.

conclusionThis study reveals novel genes and mechanisms in osimertinib resistance. The prognostic model stratifies LUAD patients effectively, and LHX2 represents a promising therapeutic target for reversing resistance.

Indexed as

Drug resistanceLHX2NSCLCOsimertinibPrognosis productive model

Identifiers

PMID41251847
PMCPMC12627286

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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.