Construction of 11 metabolic-related lncRNAs to predict the prognosis in lung adenocarcinoma

• Published in: BMC medical genomics 2023-12, Vol.16 (1), p.330-330, Article 330
• Main Authors: Jiang, Wei, Xu, Zhanyu, Huang, Liuliu, Qin, Fanglu, Yuan, Liqiang, Sun, Yu, Qin, Junqi, Deng, Kun, Zheng, Tiaozhan, Long, Xiaomao, Li, Shikang
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Adenocarcinoma - diagnosis; Adenocarcinoma - genetics; Algorithms; Analysis; Biomarkers; Cancer; Cancer therapies; Cell growth; Correlation analysis; Development and progression; Gene set enrichment analysis; Genes; Genetic aspects; Genomes; Genomics; Humans; Immune cell infiltration; Liver cancer; Long non-coding RNA; Lung; Lung adenocarcinoma; Lung cancer; Lungs; Lymphocytes; Medical prognosis; Metabolism; Metastasis; Mortality; Nomogram; Nomograms; Non-coding RNA; Predictions; Prognosis; Regression analysis; Risk assessment; Risk factors; Risk groups; Risk scoring model; RNA; RNA sequencing; RNA, Long Noncoding - genetics; Transcriptomes; Tumorigenesis
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-023-01764-9

To explore the metabolism-related lncRNAs in the tumorigenesis of lung adenocarcinoma. The transcriptome data and clinical information about lung adenocarcinoma patients were acquired in TCGA (The Cancer Genome Atlas). Metabolism-related genes were from the GSEA (Gene Set Enrichment Analysis) database. Through differential expression analysis and Pearson correlation analysis, lncRNAs about lung adenocarcinoma metabolism were identified. The samples were separated into the training and validation sets in the proportion of 2:1. The prognostic lncRNAs were determined by univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) regression. A risk model was built using Multivariate Cox regression analysis, evaluated by the internal validation data. The model prediction ability was assessed by subgroup analysis. The Nomogram was constructed by combining clinical indicators with independent prognostic significance and risk scores. C-index, calibration curve, DCA (Decision Curve Analysis) clinical decision and ROC (Receiver Operating Characteristic Curve) curves were obtained to assess the prediction ability of the model. Based on the CIBERSORT analysis, the correlation between lncRNAs and tumor infiltrating lymphocytes was obtained. From 497 lung adenocarcinoma and 54 paracancerous samples, 233 metabolic-related and 11 prognostic-related lncRNAs were further screened. According to the findings of the survival study, the low-risk group had a greater OS (Overall survival) than the high-risk group. ROC analysis indicated AUC (Area Under Curve) value was 0.726. Then, a nomogram with T, N stage and risk ratings was developed according to COX regression analysis. The C-index was 0.743, and the AUC values of 3- and 5-year survival were 0.741 and 0.775, respectively. The above results suggested the nomogram had a good prediction ability. The results based on the CIBERSORT algorithm demonstrated the lncRNAs used to construct the model had a strong correlation with the polarization of immune cells. The study identified 11 metabolic-related lncRNAs for lung adenocarcinoma prognosis, on which basis a prognostic risk scoring model was created. This model may have a good predictive potential for lung adenocarcinoma.