Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Li, Yue; Shen, Ruoyi; Wang, Anqi; Zhao, Jian; Zhou, Jieqi; Zhang, Weijie; Zhang, Ruochen; Zhu, Jianjie; Liu, Zeyi; Huang, Jian-An

doi:10.3389/fcell.2021.648806

Cited by 23 publications

(25 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 12 Time-ROC curve analysis of a 10 immune-related lncRNAs signature in the TCGA dataset in 5-year was 0.783. 9 The AUC value of ROC curve for a 4 immune-related lncRNAs prognostic signature, consisted of ITGB1-DT, ABALON, TMPO-AS1 and VIM-AS1, plotted by Zhang et al was 0.756 14 and The 5-year AUC value in another 4 immune-related lncRNAs prognostic signature including HSPC078, DRAIC, AP004608.1 and MIR223HG was 0.63. 13 The 1-year AUC value in a 5 immune-related lncRNAs signature based on TCGA was 0.686.…”

Section: Resultsmentioning

confidence: 99%

“…Although several prognostic irlncRNA signatures of lung adenocarcinoma based on the TCGA-LUAD dataset have been developed, they are all dependent on specific lncRNA expression levels of transcripts. 9–14 Therefore, this is the first study to apply a two-gene combination strategy to establish lung adenocarcinoma signatures.…”

Section: Discussionmentioning

confidence: 99%

“…The Areas Under Curve (AUC) values of Receiver Operating Characteristic (ROC) curve calculated for the LUAD dataset of The Cancer Genome Atlas database (TCGA, https://tcga-data.nci.nih.gov/tcga/ ) ranged from 0.686 to 0.783 in the established signature. All signature were constructed based on lncRNAs’ expression levels, 9–14 and two research analyzed the relationship between infiltration immune cell subtypes and risk score. 13 , 15 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Construction of a Novel Lung Adenocarcinoma Immune-Related lncRNA Pair Signature

Qi¹,

Chen²,

Chen³

et al. 2021

IJGM

View full text Add to dashboard Cite

Background A growing number of studies have demonstrated that immune-related long noncoding ribonucleic acids (irlncRNAs) are potential prognostic factors for lung adenocarcinoma. Two-gene combination patterns could improve the sensitivity of prognostic models, providing us a novel signature construction concept that we applied to lung adenocarcinoma. Methods Gene expression and clinical data were downloaded from the Lung Adenocarcinoma project of The Cancer Genome Atlas (TCGA) database. We applied a co-expression analysis with immune genes obtained from the ImmPort database to recognize irlncRNA. The matrix of irlncRNA pairs was established by a cyclic comparison of each lncRNA pair expression level. Univariate and multivariate Cox regressions and Lasso penalized regression analysis were applied to construct the risk model. Patients with lung adenocarcinoma were divided into high- and low-risk groups, according to the Akaike Information Criterion (AIC) values of the receiver operating characteristic (ROC) curve. Then, we evaluated our signature under various clinical settings: clinical-pathological characteristics, tumor-infiltrating immune cells, checkpoint-related biomarkers, targeted therapy, and chemotherapy. Results Based on the 239 differently expressed irlncRNAs, we constructed an 11-irlncRNA pair signature. The area under the curve (AUC) of the ROC curve for the signature to predict the 4-year survival rate was 0.819, and the cut-off point was recognized as 1.003. Subsequent analysis showed that our signature can effectively distinguish unfavorable survival outcomes, prognostic clinic-pathological characteristics, and specify tumor infiltration status. Highly expressed immune checkpoint-related genes, as well as higher chemosensitivity, were correlated to the low-risk group. Conclusion We constructed a novel lung adenocarcinoma irlncRNA signature with promising prognostic value using the TCGA database, based on paired irlncRNAs and not relying on lncRNAs special expression levels.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Construction of a Novel Lung Adenocarcinoma Immune-Related lncRNA Pair Signature

Qi¹,

Chen²,

Chen³

et al. 2021

IJGM

View full text Add to dashboard Cite

show abstract

“…LINC02535 is highly expressed in Lung Adenocarcinoma tissues and closely related to the OS of LUAD. Knocking down LINC02535 also signi cantly inhibited the EMT, a key contributor to tumor invasion and metastasis [25]. ADAMTS9-AS1 Restrains the Aggressive Traits of Breast Carcinoma Cells via Sponging miR-513a-5[26], but it could promotes cell proliferation and EMT in colorectal cancer [27].…”

Section: Discussionmentioning

confidence: 99%

A Novel Ferroptosis-Related Lncrnas Signature for Prognosis Prediction in Patients with Papillary Renal Cell Carcinoma

Mei

2021

Preprint

View full text Add to dashboard Cite

BackgroundPapillary renal cell carcinoma (PRCC) is a common renal cell carcinoma. Recent studies have reported that ferroptosis is involved in the occurrence and development of tumors. Long non-coding RNAs could be used as independent biomarkers for the diagnosis and prognosis of a variety of tumors, and many lncRNAs are related to the pathogenesis of PRCC. However, there are few studies on the ferroptosis-related lncRNAs of PRCC. This study aimed to establish ferroptosis-related lncRNAs prognostic signature in patients with PRCC.MethodsGene expression profile and clinical information of patients with PRCC were obtained from The Cancer Genome Atlas (TCGA) database. Lasso-Penalzed Cox regression and univariate Cox regression analysis were utilized for model construction. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Immune cell infiltration and immune function were compared between the high-risk and low-risk groups. Chemotherapy sensitivity analysis was also performed. ResultsWe constructed a prognostic signature consisted of 15 ferroptosis-related lncRNAs. The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the lncRNAs signature was 0.930, exhibiting robust prognostic capacity. The high-risk group had a greater degree of immune cell infiltration, such as B cells, T cell CD8, macrophages, NK cell, etc., compared with the low-risk group. There were significant differences in inflammation-promoting, parainflammation and Type I IFN reponse between the low-risk and high-risk groups. The expressions of immune checkpoints including CD80, IDO1, LAG3, etc. were significantly higher in high-risk group. Chemotherapy sensitivity analysis showed that MNX1-AS1, ZFAS1, MIR4435-2HG and ADAMTS9-AS1 were significantly correlated with the sensitivity of some chemotherapy drugs. ConclusionWe demonstrated that a ferroptosis-related lncRNAs prognostic signature could be a novel biomarker for PRCC. Our findings could provide a new insight for immune research and treatment strategies for patients with PRCC.

show abstract

“…They participated in the progression and metastasis of lung adenocarcinoma (LUAD) and were associated with immune pathways, and even served as a biomarker for prognosis of LUAD 10 11 . Recently, the prognostic signatures based on coding or non-coding genes to predict the prognosis of LUAD patients has been a research hot spot 12 13 . However, utilizing ARlncRNAs to construct models, and exploring the tumor immunity and the e cacy of immunotherapy in LUAD patients was still lacking.…”

Section: Introductionmentioning

confidence: 99%

Identification of an Autophagy-Related lncRNA Prognostic Signature and Related Tumor Immunity Research in Lung Adenocarcinoma

Chen

Sang

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Autophagy is closely associated with the tumor immune microenvironment (TIME) and prognosis of patients with lung adenocarcinoma (LUAD). In the present study, we established a signature based on long noncoding RNAs (lncRNAs) related to autophagy (ARlncRNAs) to investigate the TIME and survival of LUAD patients.Methods: We selected ARlncRNAs associated with prognosis to construct a model, and divided each sample into different groups based on risk score. Subsequently, Kaplan-Meier survival analysis was performed to investigate the survival outcomes of LUAD patients in different group. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis explored the enriched pathways and functions. Several bioinformatics analyses were conducted to explore the TIME of LUAD patients in different group.Results: The ARlncRNAs signature could be recognized as an independent prognostic factor for LUAD patients, and patients in the low-risk group had a greater survival advantage. GO and KEGG enrichment analysis suggested that several immune functions and pathways were enriched in different groups. A high-risk score correlated significantly negatively with high abundance of immune cells and stromal cells around the tumor and high tumor mutational burden (TMB). Low-risk patients had a higher PD-1, CTLA-4 and HAVCR2 expression, and had a better efficacy of immune checkpoint inhibitor (ICIs), including PD-1/CTLA-4 inhibitor.Conclusions: A reliable signature based on ARlncRNAs was constructed to explore the TIME and prognosis of LUAD patients, which could provide valuable information for individualized LUAD treatment.

show abstract

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Cited by 23 publications

References 42 publications

Construction of a Novel Lung Adenocarcinoma Immune-Related lncRNA Pair Signature

Construction of a Novel Lung Adenocarcinoma Immune-Related lncRNA Pair Signature

A Novel Ferroptosis-Related Lncrnas Signature for Prognosis Prediction in Patients with Papillary Renal Cell Carcinoma

Identification of an Autophagy-Related lncRNA Prognostic Signature and Related Tumor Immunity Research in Lung Adenocarcinoma

Contact Info

Product

Resources

About