Long Non-coding RNAs as Important Biomarkers in Laryngeal Cancer and Other Head and Neck Tumours

Cossu, Alessia Maria; Mosca, Laura; Zappavigna, Silvia; Misso, Gabriella; Bocchetti, Marco; Micco, Federica De; Quagliuolo, Lucio; Porcelli, Marina; Caraglia, Michele; Boccellino, Mariarosaria

doi:10.3390/ijms20143444

Cited by 70 publications

(54 citation statements)

References 83 publications

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“…7 Previously, lncRNAs have been regarded as "noise" in genome transcription; nevertheless, increasing numbers of studies have revealed their contribution to nearly all parameters of biological and pathological processes. [8][9][10] Substantial research has demonstrated an alteration of lncRNA expression in GC, with some of the identified lncRNAs performing a vital function in regulating gastric carcinogenesis and GC progression. [11][12][13] Notably, lncRNAs may serve as either tumor suppressors or oncogenic molecules and participate in the regulation of malignant characteristics of GC during GC progression.…”

Section: Introductionmentioning

confidence: 99%

Long Noncoding RNA RGMB-AS1 Acts as a microRNA-574 Sponge Thereby Enhancing the Aggressiveness of Gastric Cancer via HDAC4 Upregulation

Wang¹,

Chen²,

Tian³

et al. 2020

OTT

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Purpose: The long noncoding RNA RGMB-AS1 plays an important part in the genesis and progression of multiple human cancers. Nonetheless, little is known regarding its expression, roles, and mechanisms of action in gastric cancer (GC). This study was aimed at investigating the relationship between RGMB-AS1 and GC and illustrating the mechanisms of action of RGMB-AS1 therein. Methods: RGMB-AS1 expression in GC was measured via reverse-transcription quantitative PCR. A series of experiments including Cell Counting Kit-8 assay, flow-cytometric analysis of apoptosis, Transwell migration and invasion assays, and in vivo tumorigenesis experiment were conducted to test the effects of RGMB-AS1 on the malignant phenotype of GC cells. The molecular events behind the oncogenic actions of RGMB-AS1 in GC were elucidated through subcellular fractionation, RNA immunoprecipitation assay, bioinformatics analysis and luciferase reporter assay. Results: RGMB-AS1 upregulation was confirmed in GC tissues and cell lines. Higher RGMB-AS1 expression was associated with adverse clinical parameters and negatively correlated with patient overall survival. RGMB-AS1 knockdown inhibited GC cell proliferation, facilitated apoptosis, and reduced migration and invasion in vitro. Further experiments revealed that RGMB-AS1 knockdown decreased the tumor growth of GC cells in vivo. Mechanistically, RGMB-AS1 functioned as a competing endogenous RNA upregulating histone deacetylase 4 (HDAC4) by sponging . Rescue experiments indicated that miR-574 inhibition and HDAC4 reintroduction reversed the effects of the RGMB-AS1 knockdown on GC cells. Conclusion: The RGMB-AS1-miR-574-HDAC4 regulatory network contributes to the malignancy of GC, thereby offering a novel target for the diagnosis, prognosis, and/or treatment of GC.

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Section: Introductionmentioning

confidence: 99%

Long Noncoding RNA RGMB-AS1 Acts as a microRNA-574 Sponge Thereby Enhancing the Aggressiveness of Gastric Cancer via HDAC4 Upregulation

Wang¹,

Chen²,

Tian³

et al. 2020

OTT

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“…Estimately 50,0000 new cases are being diagnosed annually (Ghafouri-Fard et al, 2019). Despite the progress made in overall therapy in recent years, the five-year overall survival rate for HNSCC patients is only about 50% (Cossu et al, 2019;Ren et al, 2016). The poor prognosis of HNSCC patients are mainly because of local invasions, treatment-resistance, recurrence, and metastasis (Ren et al, 2016;Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

LINC00958 and HOXC13-AS as key candidate biomarkers in head and neck squamous cell carcinoma by integrated bioinformatics analysis

Xiong

Kan

et al. 2020

PeerJ

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Background Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with a strong tendency for metastasis and recurrence. Finding effective biomarkers for the early diagnosis of HNSCC is critical for the early treatment and prognosis of patients. Methods RNA sequencing data including long non-coding RNAs (lncRNAs), messenger RNA (mRNAs) and microRNAs (miRNAs) of 141 HNSCC and 44 adjacent normal tissues were obtained from the TCGA. Differentially expressed genes were analyzed using the R package DESeq. GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. A competing endogenous RNAs (ceRNA) network was constructed. The most differentially expressed genes in the main ceRNA network were chosen for nasopharyngeal carcinoma (NPC) cell lines and NPEC2 Bmi-1 cell line verification. A receiver operating characteristic (ROC) curve was constructed for 141 specimens of HNSCC tissues from 44 control samples. Results In our study, 79 HNSCC-associated abnormally expressed lncRNAs , 86 abnormally expressed miRNAs and 324 abnormally expressed mRNAs were identified. The public microarray results showed that LINC00958 and HOXC13-AS expression levels were upregulated in HNSCC tissues compared with the adjacent normal tissues in this study (p < 0.0001). LINC00958 and HOXC13-AS expression levels in NPC cell lines were higher than those in the NPEC2 Bmi-1 cell line (p < 0.05). The results showed that the area under the ROC curve (AUC) of LINC00958 reached up to 0.906 at a cutoff value of 7.96, with a sensitivity and specificity of 80.85% and 90.91%, respectively. The AUC of HOXC13-AS reached up to 0.898 at a cutoff value of 0.695, with sensitivity and specificity values of 86.23% and 83.78%, respectively. Conclusion The current study indicates that LINC00958 and HOXC13-AS are new candidate diagnostic biomarkers for HNSCC patients.

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“…The NCDB does not offer unique patient information, such as gene expression patterns or genomic sequence data, that may be predictive of patients' response to treatment. [65][66][67] The NCDB data also lacks information about treatment "quality" such as adequacy of RT given. The current literature points to the reality that drivers of survival in head and neck cancer are complex and multifactorial in nature and may be affected by treatment quality 68 in addition to socioeconomic factors and inherent disease biology.…”

Section: Discussionmentioning

confidence: 99%

Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning

et al. 2020

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Background Machine learning (ML) algorithms may predict patients who will require salvage total laryngectomy (STL) after primary radiotherapy with or without chemotherapy for laryngeal squamous cell carcinoma (SCC). Methods Patients treated for T1‐T3a laryngeal SCC were identified from the National Cancer Database. Multiple ML algorithms were trained to predict which patients would go on to require STL after primary nonsurgical treatment. Results A total of 16 440 cases were included. The best classification performance was achieved with a gradient boosting algorithm, which achieved accuracy of 76.0% (95% CI 74.5‐77.5) and area under the curve = 0.762. The most important variables used to construct the model were distance from residence to treating facility and days from diagnosis to start of treatment. Conclusion We can identify patients likely to fail primary radiotherapy with or without chemotherapy and who will go on to require STL by applying ML techniques and argue for high‐quality, multidisciplinary regionalized care.

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Long Non-coding RNAs as Important Biomarkers in Laryngeal Cancer and Other Head and Neck Tumours

Cited by 70 publications

References 83 publications

<p>Long Noncoding RNA <em>RGMB-AS1</em> Acts as a microRNA-574 Sponge Thereby Enhancing the Aggressiveness of Gastric Cancer via HDAC4 Upregulation</p>

<p>Long Noncoding RNA <em>RGMB-AS1</em> Acts as a microRNA-574 Sponge Thereby Enhancing the Aggressiveness of Gastric Cancer via HDAC4 Upregulation</p>

LINC00958 and HOXC13-AS as key candidate biomarkers in head and neck squamous cell carcinoma by integrated bioinformatics analysis

Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning

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