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ABSTRACT
The model of immune-related lncRNA pairs (IRLPs) seems to be an available predictor in lung adenocarcinoma (LUAD) patients. The aim of our study was to construct a model with IRLPs to predict the survival status and immune landscape of LUAD patients. Based on TCGA-LUAD dataset, a risk assessment model with IRLPs was established. Then, ROC curves were used to assess the predictive accuracy and effectiveness of our model. Next, we identified the difference of survival, immune cell infiltration, immune checkpoint inhibitor-related (ICI-related) biomarkers, and chemotherapeutics between high-risk group and low-risk group. Finally, A nomogram was built for predicting the survival rates of LUAD patients. 464 LUAD samples were randomly and equally divided into a training set and a test set. Six IRLPs were screened out to construct a risk model. K-M analysis and risk-plot suggested the prognosis of high-risk group was worse than low-risk group (p < 0.001). Multivariate analysis shows risk score was independent risk factor of LUAD (p < 0.001). In addition, the expression of immune cell infiltration, ICI-related biomarkers, chemotherapeutics all demonstrate significant difference in two groups. A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients. Our immune-related lncRNA pairs risk model is expected to be a reliable model for predicting the prognosis and immune landscape of LUAD patients.
Graphical Abstract
Highlights
Six immune-related lncRNA pairs (IRLPs) were used to construct the risk assessment model.
ROC curves suggested that our model was effective for predicting survival.
Half inhibitory centration (IC50) of chemotherapeutics and immune cell infiltration state were significantly different between high- and low-risk group.
Nomogram showed that higher risk score indicated worse prognosis.
The model established by paring irlncRNAs showed a promising clinical prediction value.
Data sharing statement
The data used to support the findings of this study are available from the TCGA open database (https://tcgadata.nci.nih.gov/tcga/; LUAD). GTF files were obtained from Ensembl (http://asia.ensembl.org).The the ImmPort database (http://www.immport.org) was used to retrieve immune-related genes (ir-genes) list.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Financial
This work was supported by ‘333 Project’ of Jiangsu Province (Grant number: BRA2020157), ‘Six One Project,’ Research Projects of High-level Medical Personnel of Jiangsu Province (Grant number: LGY2019025), High-level Talent Selection and Training Project of The 16th Batch of ‘Six Talent Peak’ in Jiangsu Province (Grant number: WSN-245), Medical Scientific Research Foundation of Jiangsu Commission of Health (Grant number: H2018083), Jiangsu Provincial Medical Youth Talent (Jiangsu Health Scientific Education 2017 no.3), 333 High-Level Talent Training Project (Grant number: 2016, III-0719), and High-Level Medical Talents Training Project (Grant number: 2016CZBJ042).
Author contributions
K Yuan and J Liu were responsible for study conception and design; J Liu, M Lou, and Z Gao were responsible for acquisition of data; H Wu was responsible for statistics. J Liu was responsible for drafting and revision of the manuscript.
Supplementary material
Supplemental data for this article can be accessed here.
