Distinct Patterns of mRNA and lncRNA Expression Differences Between Lung Squamous Cell Carcinoma and Adenocarcinoma

Tian, Yingxuan; Yu, Min; Sun, Li; Liu, Linghua; Wang, Jun; Hui, Ke; Nan, Qiaofeng; Nie, Xinyu; Ren, Yi; Ren, Xiaoping

doi:10.1089/cmb.2019.0164

Cited by 16 publications

(13 citation statements)

References 28 publications

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“…9,16,17 Accumulating studies have shown that different lung cancer subtypes have distinct clinical characteristics and clinical outcomes; thus, an increasing number of studies focusing on identifying signatures with noncoding RNAs have been conducted to predict the survival and immunotherapeutic response in patients with LUAD. [18][19][20] As the most abundant posttranscriptional modification in eukaryotic mRNAs and lncRNAs, m 6 A has extensive regulatory usefulness in regulating mRNA transcription, splicing, and translation and influencing the structure and effect of lncRNAs. 10 Additionally, lncRNA-related studies have drawn attention in numerous cancer fields.…”

Section: Discussionmentioning

confidence: 99%

m6A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD

Huang

et al. 2021

Molecular Therapy - Nucleic Acids

165

141

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Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide. However, the survival rate of LUAD patients remains low. N6-methyladenosine (m 6 A) and long noncoding RNAs (lncRNAs) play vital roles in the prognostic value and the immunotherapeutic response of LUAD. Thus, discerning lncRNAs associated with m 6 A in LUAD patients is critical. In this study, m 6 A-related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were conducted to construct an m 6 Arelated lncRNA model. Kaplan-Meier analysis, principalcomponent analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed. The risk model comprising 12 m 6 A-related lncRNAs was identified as an independent predictor of prognoses. By regrouping the patients with this model, we can distinguish between them more effectively in terms of the immunotherapeutic response. Finally, candidate compounds aimed at LUAD subtype differentiation were identified. This risk model based on the m 6 Abased lncRNAs may be promising for the clinical prediction of prognoses and immunotherapeutic responses in LUAD patients.

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Section: Discussionmentioning

confidence: 99%

m6A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD

Huang

et al. 2021

Molecular Therapy - Nucleic Acids

165

141

View full text Add to dashboard Cite

show abstract

“…17 In addition, differential expression analysis between lung squamous cell carcinoma and lung adenocarcinoma showed that GSEC had more corepressed relationships with differentially expressed genes, indicating that GSEC might participate in the development of lung cancer. 22 The role of GSEC in most human cancers including TNBC remains unclear. Here, we found that GSEC was upregulated in TNBC and high expression levels of GSEC were associated with advanced tumor stage, positive lymph-node metastasis and the poor survival time of TNBC patients.…”

Section: Discussionmentioning

confidence: 99%

lncRNA GSEC Promotes the Progression of Triple Negative Breast Cancer (TNBC) by Targeting the miR-202-5p/AXL Axis

Zhang¹,

Du²,

Zhang³

et al. 2021

OTT

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Background: This study aimed to explore the biological functions of G-quadruplex-forming sequence containing lncRNA (GSEC) in triple negative breast cancer (TNBC). Methods: The expression of GSEC in TNBC tissues was evaluated by qRT-PCR. Cell viability was evaluated by Cell Counting Kit-8 assay. Cell proliferation was evaluated by 5-ethynyl-20-deoxyuridine (EdU) staining assay. Cell invasion and migration were evaluated by Transwell assay. Gain-and loss-function assays were performed to assess the biological functions of GSEC in TNBC. The interactions between GSEC, miR-202-5p and AXL were determined by luciferase report assay and RNA immunoprecipitation (RIP) assay. In addition, a nude mouse xenograft model was used to confirm the oncogenic role of GSEC in TNBC. Results: GSEC was significantly upregulated in TNBC tissues and cancer cell lines, and high level of GSEC was associated with advanced tumor stage, positive lymph-node metastasis and the poor prognosis of TNBC patients. Knockdown of GSEC effectively inhibited TNBC cell proliferation, invasion and migration in vitro. GSEC regulated the expression of AXL by directly sponging miR-202-5p. Downregulation of miR-202-5p attenuated GSEC knockdown-induced inhibition on TNBC cell proliferation, invasion and migration in vitro. Meanwhile, overexpression of AXL obviously reversed the inhibitory effects of miR-202-5p mimics in TNBC progression in vitro. Conclusion: GSEC functioned as a potential oncogene and promoted AXL-mediated TNBC progression by sponging miR-202-5p, which might be a novel diagnostic and therapeutic target for TNBC.

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“…The literures showed that MIR3945HG was identi ed as optimal diagnostic lncRNA biomarkers for lung adenocarcinoma and lung squamous cell carcinoma [25]. TXNRD1 Is an Unfavorable Prognostic Factor for Patients with Hepatocellular Carcinoma [26,27].…”

Section: Discussionmentioning

confidence: 99%

Construction of Co-Expression Modules Related to Survival by WGCNA and Identification of Potential Prognostic Biomarkers in Liver Cancer

Zhang

Feng

Wang

et al. 2021

Preprint

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Background Many recent studies have reported the role of WGCNA in liver cancer, and screened out modules related to the clinical characteristics of liver cancer. The prognostic role of modular genes provides a reference for the treatment of liver cancer and the study of survival time extension. MethodsUse edgeR to screen differentially expressed genes, and use weighted gene co-expression network analysis (WGCNA) method to divide differential genes into modules; combined with clinical data, select gene modules that are highly related to clinical information to construct a prognostic model. Use univariate Cox and multivariate Cox regression analysis to predict the value of prognosis. The Kaplan-Meier method and ROC curves were used to evaluate the value of features of predicting prognosis.ResultsIn the co-expression analysis of liver cancer, the two gene modules darkrey and navajowhite2 was highly correlated with the TNM stag of liver cancer. In addition, they were related to the reduction process and the activation of specific DNA-binding transcription factors. Constructing a risk ratio model with these two modules shows that the overall survival time of patients in the high-risk group is lower than that of the low-risk group. In the module, ABLIM2, RP11-109J4.1, TXNRD1, C9orf106, LINC00677, CYP11B2, AC005550.3 and MIR3945HG belong to the Hub gene, and can also be used as independent prognostic factors, which may become potential biomarkers for the diagnosis and treatment of liver cancer. Conclusion This studies identified and screened the prognostic genes RP11-109J4.1, TXNRD1, C9orf106, LINC00677, CYP11B2, AC005550.3 and MIR3945HG related to the progression of liver cancer, which can provide a reference form the diagnosis and treatment of liver cancer.

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Distinct Patterns of mRNA and lncRNA Expression Differences Between Lung Squamous Cell Carcinoma and Adenocarcinoma

Cited by 16 publications

References 28 publications

m6A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD

m6A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD

lncRNA GSEC Promotes the Progression of Triple Negative Breast Cancer (TNBC) by Targeting the miR-202-5p/AXL Axis

Construction of Co-Expression Modules Related to Survival by WGCNA and Identification of Potential Prognostic Biomarkers in Liver Cancer

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