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

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.

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Journal
Medicine
Published
2025-04-11
DOI
https://doi.org/10.1097/md.0000000000042122
Citations
3
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
2.81
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article

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

Adili Salai, Shengli Gao, Dongbo Luo, Hongbo Lv et al.
3 citations
Medicine
Cancer-related molecular mechanisms research
2.81
article

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

Adili Salai, Shengli Gao, Dongbo Luo, Hongbo Lv, Yi Liu, Yufei Wang, Abulajiang Kamili, Dilimulai Aishanjiang
article en
3 citations

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.

MedicineVol. 104(15)
Xinjiang Medical University (CN), Tumor Hospital of Xinjiang Medical University (CN), Second Affiliated Hospital of Xinjiang Medical University (CN)
Good health and well-being
Openalex Percentile: Top 10%
Cancer-related molecular mechanisms research
2.81
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