Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes

Zheng, Zhihong; Deng, Weijie; Yang, Jiansheng

doi:10.1155/2020/8832739

Cited by 22 publications

(4 citation statements)

References 41 publications

(21 reference statements)

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“…The four types of ISs presented different molecular characteristics, which were reflected in the differences in the number of TMB, mutant genes, chemokines and chemokine receptors. We observed that TP53 mutations were the most common, which was consistent with previous studies ( 38 ). A growing body of findings supported the correlation of differential existence of components of the immune system in deciding the evolution of cancer ( 39 ).…”

Section: Discussionsupporting

confidence: 93%

Identification of an Immune Classification and Prognostic Genes for Lung Adenocarcinoma Based on Immune Cell Signatures

et al. 2022

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ObjectiveCurrent advances in immunotherapy requires accurate tumor sub-classification due to the heterogeneity of lung adenocarcinoma (LUAD). This study aimed to develop a LUAD sub-classification system based on immune cell signatures and identified prognostic gene markers.MethodsSignatures related to the prognosis of TCGA-LUAD and 4 GSE cohorts were screened and intersected from 184 previously published immune cell signatures. The LUAD samples in the TCGA were clustered by ConsensusClusterPlus. Molecular characteristics, immune characteristics and sensitivity to immunotherapies/chemotherapies were compared. LDA score was established through Linear Discriminant Analysis (LDA). Co-expression module was constructed by Weighted Gene Co-Expression Network Analysis (WGCNA).ResultsFour LUAD subtypes with different molecular and immune characteristics were identified. Significant differences in prognosis among the four subtypes were observed. The IS1 subtype with the worst prognosis showed the highest number of TMB, mutant genes, IFN γ score, angiogenesis score and immune score. Twenty co-expression modules were generated by WGCNA. Blue module, sky blue module and light yellow module were significantly correlated with LUAD prognosis. The hub genes (CCDC90B, ARNTL2, RIPK2, SMCO2 and ADA and NBN) showing great prognostic significance were identified from the blue module. A total of 8 hub genes (NLRC3, CLEC2D, GIMAP5, CXorf65, PARP15, AKNA, ZC3H12D, and ARRDC5) were found in the light yellow module. Except for CXorf65, the expression of the other seven genes were significantly correlated with LUAD prognosis.ConclusionThis study determined four LUAD subtypes with different molecular and immune characteristics and 13 genes closely related to the prognosis of LUAD. The current findings could help understand the heterogeneity of LUAD immune classes.

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Section: Discussionsupporting

confidence: 93%

Identification of an Immune Classification and Prognostic Genes for Lung Adenocarcinoma Based on Immune Cell Signatures

et al. 2022

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“…To further assess the relationship between the risk score and immune prognosis, we examined the PD-L1 expression levels between the two risk subgroups and found that PD-L1 expression levels were significantly higher in the high-risk group than in the low-risk group, consistent with the fact that Luo et al also showed significant differences in PD-L1 expression between the low- and high-risk groups in patients with LUAD ( Luo et al, 2020 ). In addition, we compared the mutation characteristics of the high-risk and low-risk groups and found that TP53 mutations were more common in the high-risk population, which was also identified in one study that found a higher frequency of TP53 mutations in the C1 subtype of LUAD ( Zheng et al, 2020 ). Finally, we validated the expression of prognostic lncRNAs in clinical LUAD tissues and found expression trends consistent with two public databases, with one study showing that LINC00857 expression was significantly upregulated in LUAD paraneoplastic tissues compared to normal lung tissue ( Wang et al, 2020 ), which is consistent with our results.…”

Section: Discussionmentioning

confidence: 63%

Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

et al. 2023

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Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors.Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays.Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues.Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients.

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“…8 Zheng et al developed prognostic risk models based on differentially expressed IRGs in different molecular subtypes of LUAD. 9 Therefore, studying invasion-related markers to ensure earlier treatment can potentially improve the prognosis and survival of cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Identification Invasion-Related Long Non-Coding RNAs in Lung Adenocarcinoma and Analysis of Competitive Endogenous RNA Regulatory Networks

Cai¹,

Jiang²,

Zhu³

2023

IJGM

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Background: Cell invasion plays a vital role in cancer development and progression. Aberrant expression of long non-coding RNAs (lncRNAs) is also critical in carcinogenesis. However, the prognostic value of invasion-related lncRNAs in lung adenocarcinoma (LUAD) remains unknown. Methods: Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and microRNAs (DEmiRNAs) were between LUAD and control samples. Pearson correlation analyses were performed to screen for invasion-related DElncRNAs (DEIRLs). Univariate and multivariate Cox regression algorithms were applied to identify key genes and construct the risk score model, which was evaluated using receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was used to explore the underlying pathways of the risk model. Moreover, an invasion-related competitive endogenous RNA (ceRNA) regulatory network was constructed. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to detect the expression of prognostic lncRNAs in the LUAD and control samples. Results: A total of 45 DElncRNAs were identified as DEIRLs. RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3 were potential prognostic lncRNAs, the expression of which was verified by RT-qPCR in LUAD samples. Both the risk score model and nomogram used the prognostic lncRNAs. ROC curves showed the risk score model had moderate accuracy and the nomogram had high accuracy in predicting patient prognosis. GSEA results indicated that the risk score model was associated with many biological processes and pathways relevant to cell proliferation. A ceRNA regulatory network was constructed in which PDZRN3-miR-96-5p-CPEB1, EP300-AS1-miR-93-5p-CORO2B, and RP3-525N10.2-miR-130a-5p-GHR may be key invasionrelated regulatory pathways in LUAD. Conclusion:Our study identified five novel invasion-related prognostic lncRNAs (RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3) and established an accurate model for predicting the prognosis of patients with LUAD. These findings enrich our understanding of the relationships between cell invasion, lncRNAs, and LUAD and may provide novel treatment directions.

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Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes

Cited by 22 publications

References 41 publications

Identification of an Immune Classification and Prognostic Genes for Lung Adenocarcinoma Based on Immune Cell Signatures

Identification of an Immune Classification and Prognostic Genes for Lung Adenocarcinoma Based on Immune Cell Signatures

Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

Identification Invasion-Related Long Non-Coding RNAs in Lung Adenocarcinoma and Analysis of Competitive Endogenous RNA Regulatory Networks

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