A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Abstract

Background Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD). Methods RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method. Results A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance ( P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis. Conclusions We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

A prognostic model for melanoma patients on the basis of immune-related lncRNAs

“…iv) they regulate differentiation and activation of T cells and B cells [ 13 ]; v) they participate in the activation of autophagy, and other inflammation-associated processes [ 14 ]. Immune-related lncRNAs have predictive value for survival and prognosis of a variety of tumors and are potential targets for cancer treatment [ 15 , 16 ].…”

A Novel Prognostic Signature of Immune-related lncRNA Pairs in Lung Adenocarcinoma

“…[27][28][29] In particular, immune-related signatures have been proved to have effectively predictive value in the treatment and prognosis of cancers including LUAD. [16][17][18][19][20] However, most of these signatures depend on the exact expression levels of transcripts, which weakens their clinical application value due to the heterogeneity of gene expression data.…”

“…15 Furthermore, recent studies have indicated that signatures of irlncRNAs could serve as prognostic biomarkers of the survival of cancers, such as breast cancer, colon cancer, pancreatic cancer as well as LUAD. [16][17][18][19][20] By using the gene expression data and clinical data in The Cancer Genome Atlas (TCGA), Miao et al constructed a signature of immune-related six-lncRNAs to predict the OS of patients with LUAD and this signature could act as an independent prognostic factor. 19 Similarly, Li et al…”

“…identi ed a seven irlncRNA signature for predicting OS of LUAD patients and this prognostic model could well distinguish good or poor survival of patients with LUAD. 20 However, the majority of these lncRNA signatures are based on the quantitative expression levels of lncRNAs, and different experimental platforms and batches may lead to the heterogeneity of gene expression data, which weakens the clinical application value of these lncRNA signatures.…”

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“…As in previous studies (Li et al, 2020), univariate Cox regression (p < 0.05) was used to screen out the ubiquitination-related genes that were significantly associated with the prognosis of pancreatic cancer patients from the DRGs. Multivariate Cox proportional hazards regression analysis (with forwarding selection and backward selection) was then used to analyze these ubiquitination-related genes selected by univariate Cox regression.…”