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

Content Provider	Springer Nature : BioMed Central
Author	Jiang, Wei Xu, Zhanyu Huang, Liuliu Qin, Fanglu Yuan, Liqiang Sun, Yu Qin, Junqi Deng, Kun Zheng, Tiaozhan Long, Xiaomao Li, Shikang
Abstract	Objective To explore the metabolism-related lncRNAs in the tumorigenesis of lung adenocarcinoma. Methods 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. Results 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. Conclusions 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.
Related Links	https://bmcmedgenomics.biomedcentral.com/counter/pdf/10.1186/s12920-023-01764-9.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	17558794
DOI	10.1186/s12920-023-01764-9
Journal	BMC Medical Genomics
Issue Number	1
Volume Number	16
Language	English
Publisher	BioMed Central
Publisher Date	2023-12-18
Access Restriction	Open
Subject Keyword	Human Genetics Microarrays Gene Expression Long non-coding RNA Lung adenocarcinoma Nomogram Risk scoring model Immune cell infiltration
Content Type	Text
Resource Type	Article
Subject	Genetics (clinical) Genetics
Journal Impact Factor	2.1/2023
5-Year Journal Impact Factor	2.5/2023

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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