Identification of lncRNAs associated with lung squamous cell carcinoma prognosis in the competitive endogenous RNA network

Qi, Ling‐Yu; Zhang, Tingting; Yao, Yan; Zhuang, Jing; Liu, Cun; Liu, Ruijuan; Sun, Changgang

doi:10.7717/peerj.7727

Cited by 19 publications

(12 citation statements)

References 55 publications

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“…Our findings that lncRNAs can accurately discriminate BrCa from health indirectly support their contribution to tumor biology. The lncRNA AC078802.1 is an antisense to ACTRT3 and its aberrant expression has only been demonstrated in squamous lung cancer, presenting contradictory results so far [ 34 , 35 ]. Our methylome bioinformatics analysis revealed that AC078802.1 is hypermethylated in BrCa, opening the field for further analysis.…”

Section: Discussionmentioning

confidence: 99%

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Παναγοπούλου

Karaglani

Manolopoulos

et al. 2021

Cancers

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DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982–1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921–1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920–1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

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Section: Discussionmentioning

confidence: 99%

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Παναγοπούλου

Karaglani

Manolopoulos

et al. 2021

Cancers

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“…Zhang et al (2019) demonstrated that lncRNA ERVH48-1 may be act as a latent signature of prognosis and treatment for tongue squamous cell carcinoma patients. Qi et al (2019) confirmed ERVH48-1 as a latent prognostic element for lung squamous cell carcinoma patients based on the ceRNA network analysis. Our study is the first report of the key role of ERVH48-1 in ceRNA networks, and its dysregulation is crucial for the development of BRCA aged ≥ 65 years.…”

Section: Discussionmentioning

confidence: 52%

Construction and Comparison of ceRNA Regulatory Network for Different Age Female Breast Cancer

et al. 2021

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Studies have shown the difference appearing among the prognosis of patients in different age groups. However, the molecular mechanism implicated in this disparity have not been elaborated. In this study, expression profiles of female breast cancer (BRCA) associated mRNAs, lncRNAs and miRNAs were downloaded from the TCGA database. The sample were manually classified into three groups according to their age at initial pathological diagnosis: young (age ≤ 39 years), elderly (age ≥ 65 years), and intermediate (age 40–64 years). lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network was respectively constructed for different age BRCA. Then, the biological functions of differentially expressed mRNAs (DEmRNAs) in ceRNA network were further investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, survival analysis was used to identify prognostic biomarkers for different age BRCA patients. We identified 13 RNAs, 38 RNAs and 40 RNAs specific to patients aged ≤ 39 years, aged 40–64 years, and aged ≥ 65 years, respectively. Furthermore, the unique pathways were mainly enriched in cytokine-cytokine receptor interaction in patients aged 40–64 years, and were mainly enriched in TGF-beta signaling pathway in patients aged ≥ 65 years. According to the survival analysis, AGAP11, has-mir-301b, and OSR1 were respectively functioned as prognostic biomarkers in young, intermediate, and elderly group. In summary, our study identified the differences in the ceRNA regulatory networks and provides an effective bioinformatics basis for further understanding of the pathogenesis and predicting outcomes for different age BRCA.

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“…Furthermore, they showed that overexpression of LINC00261 reduced β-catenin and TCF4 expression and simultaneously increased E-cadherin, which is a TCF4 downstream target. In addition, other studies suggested that LINC00261 might act as a sponge for several miRNAs, as it was found in several comprehensive analyses of ceRNA networks in different types of cancer [73][74][75]. However, stringent experimental evidence that would clearly support a ceRNA function of LINC00261 is still lacking.…”

Section: Discussionmentioning

confidence: 97%

LINC00261 Is Differentially Expressed in Pancreatic Cancer Subtypes and Regulates a Pro-Epithelial Cell Identity

Dorn

Glaß

Neu

et al. 2020

Cancers

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Pancreatic adenocarcinoma (PDAC) is one of the major causes of cancer-associated deaths worldwide, with a dismal prognosis that has not significantly changed over the last decades. Transcriptional analysis has provided valuable insights into pancreatic tumorigenesis. Specifically, pancreatic cancer subtypes were identified, characterized by specific mutations and gene expression changes associated with differences in patient survival. In addition to differentially regulated mRNAs, non-coding RNAs, including long non-coding RNAs (lncRNAs), were shown to have subtype-specific expression patterns. Hence, we aimed to characterize prognostic lncRNAs with deregulated expression in the squamous subtype of PDAC, which has the worst prognosis. Extensive in silico analyses followed by in vitro experiments identified long intergenic non-coding RNA 261 (LINC00261) as a downregulated lncRNA in the squamous subtype of PDAC, which is generally associated with transforming growth factor β (TGFβ) signaling in human cancer cells. Its genomic neighbor, the transcription factor forkhead box protein A2 (FOXA2), regulated LINC00261 expression by direct binding of the LINC00261 promoter. CRISPR-mediated knockdown and promoter knockout validated the importance of LINC00261 in TGFβ-mediated epithelial–mesenchymal transition (EMT) and established the epithelial marker E-cadherin, an important cell adhesion protein, as a downstream target of LINC00261. Consequently, depletion of LINC00261 enhanced motility and invasiveness of PANC-1 cells in vitro. Altogether, our data suggest that LINC00261 is an important tumor-suppressive lncRNA in PDAC that is involved in maintaining a pro-epithelial state associated with favorable disease outcome.

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Identification of lncRNAs associated with lung squamous cell carcinoma prognosis in the competitive endogenous RNA network

Cited by 19 publications

References 55 publications

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Construction and Comparison of ceRNA Regulatory Network for Different Age Female Breast Cancer

LINC00261 Is Differentially Expressed in Pancreatic Cancer Subtypes and Regulates a Pro-Epithelial Cell Identity

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