Evidence map›Paper›PMID 40228246›Full record

ArticleMedicine2025

Construction of a prognostic model for autophagy-related LncRNAs in lung adenocarcinoma.

Yang Wang, Adili Salai, Dongbo Luo, Hongbo Lv, Shengli Gao, Abulajiang Kamili, Dilimulai Aishanjiang, Yi Liu

Abstract read
In one paragraph

Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yang WangThe Second Department of Thoracic Surgery, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, China.
Adili Salai
Dongbo Luo
Hongbo Lv
Shengli Gao
Abulajiang Kamili
Dilimulai Aishanjiang

Funding

the Natural Science Foundation of Xinjiang Uygur Autonomous Region 2022D01C533
6 · The paper itself

Abstract

Lung cancer remains the leading cause of cancer-related mortality globally, with lung adenocarcinoma being the most prevalent subtype. Current prognostic indicators have limitations due to tumor heterogeneity, necessitating the identification of novel biomarkers for better risk stratification and personalized treatment. Here, we constructed and validated a prognostic model for lung adenocarcinoma based on autophagy-related long noncoding RNAs (LncRNAs). Transcriptional data, including 501 lung adenocarcinoma and 54 adjacent non-tumor samples, were retrieved from the cancer genome atlas. The LncRNAs associated with autophagy-related genes were identified. A prognostic prediction model was constructed using univariate Cox regression and further refined through the Lasso regression. The risk score, calculated based on the prediction model, was used to stratify patients into high-risk and low-risk groups. The prognostic value of the model was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Twenty paired lung adenocarcinoma and adjacent noncancerous tissues were collected from patients who underwent surgery. Six LncRNAs were validated in these tissues using RT-qPCR. A total of 1321 autophagy-related LncRNAs (R ≥ 0.3, P < .001) were identified, with 143 LncRNAs significantly associated with the prognosis of lung adenocarcinoma. A prognostic prediction model, composed of 14 LncRNAs (LINC01876, FAM83A-AS1, AL031667.3, FENDRR, AC125807.2, AP002761.1, AC107959.3, MYO16-AS1, AL606489.1, AC026355.2, NKILA, LINC01116, LINC01137, and MMP2-AS1), was constructed. The high-risk group had significantly lower survival times than the low-risk group (P < .001). The area under ROC curves of the prognostic model was 0.78, 0.73, and 0.71 for 1-year, 2-year, and 3-year survival, respectively. Consistently, RT-qPCR revealed that LINC01876, AC125807.2, and AL031667.3 were significantly increased in lung adenocarcinoma, while MMP2-AS1, AC026355.2, and FENDRR were significantly decreased. The study presents a novel prognostic model based on 14 autophagy-related LncRNAs for patients with lung adenocarcinoma. This model may further guide the clinical treatment of lung adenocarcinoma.

Indexed as

Adenocarcinoma of LungAutophagyLung NeoplasmsRNA, Long NoncodingAgedBiomarkers, TumorFemaleHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisROC CurveBiomarkers, TumorRNA, Long Noncodingautophagylong noncoding RNAlung adenocarcinomaprediction modelprognosis

Identifiers

PMID40228246
PMCPMC11999454

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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.