Five long non-coding RNAs establish a prognostic nomogram and construct a competing endogenous RNA network in the progression of non-small cell lung cancer

Abstract

Background Accumulating evidence has revealed that long non-coding RNAs (lncRNAs) play vital roles in the progression of non-small cell lung cancer (NSCLC). But the relationship between lncRNAs and survival outcome of NSCLC remains to be explored. Therefore, we attempt to figure out their survival roles and molecular connection in NSCLC. Methods By analyzing the transcriptome profiling of NSCLC from TCGA databases, we divided patients into three groups, and identified differentially expressed lncRNAs (DELs) of each group. Next, we explored the prognostic roles of common DELs by univariate and multivariate Cox analysis, Lasson, and Kaplan-Meier analysis. Additionally, we assessed and compared the prognostic accuracy of 5 lncRNAs through ROC curves and AUC values. Ultimately, we detected their potential function by enrichment analysis and molecular connection through establishing a competing endogenous RNA (ceRNA) network. Results One hundred ninety-seven common DELs were spotted. And we successfully screened out 5 lncRNAs related to the patient’s survival, including LINC01833, AC112206.2, FAM83A-AS1, BANCR, and HOTAIR. Combing with age and AJCC stage, we constructed a nomogram that prognostic prediction was superior to the traditional parameters. Furthermore, 275 qualified mRNAs related to 5 lncRNAs were spotted. Functional analysis indicates that these lncRNAs act key roles in the progression of NSCLC, such as P53 and cell cycle signaling pathway. And ceRNA network also suggests that these lncRNAs are tightly connected with tumor progression. Conclusions A nomogram and ceRNA network based on 5 lncRNAs indicate that there can effectively predict the overall survival of NSCLC and potentially serve as a therapeutic guide for NSCLC.

A nomogram based on A-to-I RNA editing predicting overall survival of patients with lung squamous carcinoma

“…Generally, the nomogram exerted a medium accuracy on predicting OS of LUSC, displaying a better overall net benefit than the T, N stating system for predicting 1- and 3-year OS rate. Although in terms of validity, this ATIRE-based nomogram did not display a superior performance compared to previously published gene-expression-based nomograms as shown by the Harrell’s C-indexes, given an amount of gene-expression-based nomograms exerted Harrell’s C-indexes ranging from 0.65–0.85 [ 44 – 49 ]. However, in terms of determination reliability, quantification of AITRE level is more stable and reliable than that of gene expression.…”

Methyltransferase 3, N6-adenosine-methyltransferase complex catalytic subunit-induced long intergenic non-protein coding RNA 1833 N6-methyladenosine methylation promotes the non-small cell lung cancer progression via regulating heterogeneous nuclear ribonucleoprotein A2/B1 expression

“…In the previous studies, Yu et al identified five lncRNAs which showed important prognostic values in NSCLC. LINC01833 showed the most significant upregulation in the NSCLC tissues compared with control group [ 17 ]. The present study shows that overexpression of LINC01833 promoted cell proliferative, invasive, and migratory capabilities, inhibited cell apoptosis, and promoted tumorigenesis, suggesting that LINC01833 can promote NSCLC progression.…”

“…In addition, several lncRNAs were revealed as important biomarkers in NSCLC [ 13 , 14 ], such as long intergenic non-protein coding RNA 504 and long intergenic non-protein coding RNA 691 [ 15 , 16 ]. Recently, Yu et al successfully identified five survival-related lncRNAs using transcriptome profiling analysis of NSCLC from TCGA database [ 17 ]. Among them, the expression of long intergenic non-protein coding RNA 1833 (LINC01833) showed the most significant upregulation in tumors compared with normal group, suggesting that LINC01833 might act as an oncogene in NSCLC.…”

Identification of lncRNA–miRNA–mRNA Networks Linked to Non-small Lung Cancer Resistance to Inhibitors of Epidermal Growth Factor Receptor

“…And, many studies have carried out a comprehensive analysis of ncRNA and its related co-expression and ceRNA network differential expression profiles in NSCLC. Yu and Ren (2021) constructed a ceRNA network by analyzing the differentially expressed lncRNAs of NSCLC transcriptome profiling from TCGA. The network can effectively predict the overall survival of NSCLC and may guide the treatment of NSCLC.…”