LOXL1-AS1 contributes to the proliferation and migration of laryngocarcinoma cells through miR-589-5p/TRAF6 axis

Zhai

Analytical Cellular Pathology

et al. 2020

Objective. To investigate the expression of long noncoding RNA lysyl oxidase-like 1-antisense 1 (LOXL1-AS1) in hepatocellular carcinoma tissues and its effect on cell proliferation, migration, and invasion. Methods. Quantitative real-time PCR was used to analyze the expression of LOXL1-AS1 RNA in tumor tissues, adjacent normal tissues, and cell lines. MTT assay, colony formation assay, flow cytometry analysis, transwell assays, and lentivirus-mediated RNA interference (RNAi) technology were used to evaluate cell proliferation and migration. Results. In the present study, we observed that the expression level of LOXL1-AS1 in hepatocellular carcinoma tissue was significantly higher than that in adjacent nontumor tissues, and its expression in three hepatic carcinoma cell lines was obviously higher than that in a normal cell line. In addition, in the Hep-G2 cell line, LOXL1-AS1 downregulation significantly inhibited cell proliferation in the light of the MTT and colony formation assays in vitro, which was consistent with animal experiment in vivo. What is more, cell migration was also inhibited in vitro in Matrigel Transwell Assay by LOXL1-AS1 knockdown, which might be partly attributed to the reduction of MMP-2 and MMP-9 protein expressions. Finally, cell cycle analysis revealed that knockdown of LOXL1-AS1 induced significantly a G0/G1 phase cell cycle arrest, which might be partly attributed to the downregulation of Cdc2, Cdc25A, and cyclin B1 protein expression. Conclusion. In conclusion, we demonstrated that reduced LOXL1-AS1 expression could inhibit hepatocellular carcinoma cell proliferation, migration, and invasion. The application of RNAi targeting LOXL1-AS1 might be a potential treatment strategy in advanced cases.

Section: Discussionmentioning

confidence: 99%

The Long Noncoding RNA LOXL1-AS1 Promotes the Proliferation, Migration, and Invasion in Hepatocellular Carcinoma

Zhai

Analytical Cellular Pathology

et al. 2020

“…LSCC, which is of epithelial origin, is a most common tumor of the upper respiratory tract [23]. Poor living habits, such as imbalanced diet, smoking, and alcohol consumption, are the main risk factors contributing to laryngeal cancer [24].…”

Section: Discussionmentioning

confidence: 99%

A Novel Signature Derived from Metabolism-Related Genes GPT and SMS to Predict Prognosis of Laryngeal Squamous Cell Carcinoma

Shen¹,

Huang²,

Zhang³

et al. 2021

Preprint

Background A growing body of evidence has suggested the involvement of metabolism in the occurrence and development of tumors. But the link between metabolism and laryngeal squamous cell carcinoma (LSCC) has rarely been reported. This study seeks to understand and explain the role of metabolic biomarkers in predicting the prognosis of LSCC. Methods We identified the differentially expressed metabolism-related genes (MRGs) through RNA-seq data of TCGA (The Cancer Genome Atlas) and GSEA (Gene set enrichment analysis). After the screening of protein-protein interaction (PPI), hub MRGs were analyzed by least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic signature. Kaplan–Meier survival analysis and the receiver operating characteristic (ROC) was applied to verify the effectiveness of the prognostic signature in four cohorts (TCGA cohort, GSE27020 cohort, TCGA-sub1 cohort and TCGA-sub2 cohort). The expressions of the hub MRGs in cell lines and clinical samples were verified by quantitative reverse transcriptase PCR (qRT-PCR). The immunofluorescence staining of the tissue microarray (TMA) was carried out to further verify the reliability and validity of the prognostic signature. Cox regression analysis was then used to screen for independent prognostic factors of LSCC and a nomogram was constructed based on the results. Results Among the 180 differentially expressed MRGs, 14 prognostic MRGs were identified. A prognostic signature based on two MRGs (GPT and SMS) was then constructed and verified via internal and external validation cohorts. Compared to the adjacent normal tissues, SMS expression was higher while GPT expression was lower in LSCC tissues, indicating poorer outcomes. The risk score proved the prognostic signature as an independent risk factor for LSCC in both internal and external validation cohorts. A nomogram based on these results was developed for clinical application. Conclusions Differentially expressed MRGs were found and proven to be related to the prognosis of LSCC. We constructed a novel prognostic signature based on MRGs in LSCC for the first time and verified via different cohorts from both databases and clinical samples. A nomogram based on this prognostic signature was developed.

“…Then, the cells were rinsed with PBS three times to remove floating cells, and incubated in serum-free DMEM to inhibit cell proliferation and division. At 0 and 24 h after wounding and culture, the migration distance of cells in the wound area was observed under a microscope, and several different fields of view were randomly selected for photographing (He et al, 2020). The experiment was repeated three times.…”

Section: Wound Healing Assaymentioning

confidence: 99%

Bioinformatics analysis of laryngeal squamous cell carcinoma: seeking key candidate genes and pathways

Ma¹,

Hu²,

Dai³

et al. 2021

PeerJ

Background Laryngeal squamous cell carcinoma (LSCC) is the second most aggressive head and neck squamous cell carcinoma. Although much work has been done to optimize its treatment, patients with LSCC still have poor prognosis. Therefore, figuring out differentially expressed genes (DEGs) contained in the progression of LSCC and employing them as potential therapeutic targets or biomarkers for LSCC is extremely meaningful. Methods Overlapping DEGs were screened from two standalone Gene Expression Omnibus datasets, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. By applying STRING and Cytoscape, a protein–protein network was built, and module analysis was carried out. The hub genes were selected by maximal clique centrality with the CytoHubba plugin of Cytoscape. UALCAN and GEPIA data were examined to validate the gene expression findings. Moreover, the connection of the hub genes with LSCC patient overall survival was studied employing The Cancer Genome Atlas. Then, western blot, qRT-PCR, CCK-8, wound healing and transwell assays were bring to use for further verify the key genes. Results A total of 235 DEGs were recorded, including 83 upregulated and 152 downregulated genes. A total of nine hub genes that displayed a high degree of connectivity were selected. UALCAN and GEPIA databases verified that these genes were highly expressed in LSCC tissues. High expression of the SPP1, SERPINE1 and Matrix metalloproteinases 1 (MMP1) genes was connected to worse prognosis in patients with LSCC, according to the GEPIA online tool. Western blot and qRT-PCR testify SPP1, SERPINE1 and MMP1 were upregulated in LSCC cells. Inhibition of SPP1, SERPINE1 and MMP1 suppressed cell proliferation, invasion and migration. Conclusion The work here identified effective and reliable diagnostic and prognostic molecular biomarkers by unified bioinformatics analysis and experimental verification, indicating novel and necessary therapeutic targets for LSCC.