M6A reader YTHDF1 promotes malignant progression of laryngeal squamous carcinoma through activating the EMT pathway by EIF4A3

Guo, Huina; Han, Qi; Guan, Xiaoya; Li, Zhongxun; Wang, Ying; He, Long; Guo, Yujia; Zhao, Liting; Xue, Xuting; Liu, Hongliang; Zhang, Chunming

doi:10.1016/j.cellsig.2023.111002

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Furthermore, m6A modi cation has been identi ed as intimately linked to tumorigenesis and disease progression in LC research (12,34). Subsequent research has revealed that the m6A modi cation may in uence the incidence and therapeutic e cacy of LC by regulating pivotal gene expression (35,36). In summary, m6A modi cation is important in cellular biology and cancer investigation, providing valuable insights into the etiology and treatment approaches for associated diseases (37).…”

Section: Discussionmentioning

confidence: 99%

Identification of m6A-Regulated Ferroptosis Biomarkers for Prognosis in Laryngeal Cancer

Wang,

Zhang,

Liang

et al. 2024

Preprint

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Background N6-methyladenosine (m6A) RNA methylation and ferroptosis are associated with laryngeal cancer (LC) development. Accordingly, further research on related molecular mechanisms and pathology of LC is necessary. Methods Weighted gene co-expression network analysis (WGCNA) and correlation analysis were used to identify differentially expressed m6A-related ferroptosis genes (DE-MRFG) in LC. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression were utilized for feature selection and risk model construction. Then, a nomogram was built based on the independent prognostic factor identified using univariate and multivariate Cox regression. Mutation analysis, immune-related analysis, and drug sensitivity prediction were applied to analyze the utility of the risk model as much as possible. Additionally, qRT-PCR and western blot were performed to detect the TFRC, RGS4, and FTH1 expression. Results We identified 83 genes as DE-MRFG in LC. Three model genes (TFRC, RGS4, and FTH1) were identified to build a risk model using the univariate Cox and LASSO regression algorithms. Receiver operating characteristic (ROC) analysis verified the accuracy of the risk model. Furthermore, calibration curves and ROC analysis indicated the good performance of the nomogram in predicting overall survival (OS). Moreover, the mutation analysis indicated that multiple genes were mutated in the high- and low-risk groups. Based on the analysis of the immune reaction in LC, immune checkpoint PD-L1 was significantly related to the risk score and was up-regulated in the high-risk group. Tumor Immune Dysfunction and Exclusion (TIDE) and Estimation of STromal and Immune cells in MAlignant Tumors using the Expression data (ESTIMATE) algorithm showed a positive relationship between risk score and TIDE or ESTIMATE score. Furthermore, drug sensitivity prediction found that 19 chemotherapy drugs were strongly correlated with a risk score. TFRC, RGS4, and FTH1 exhibited high expression levels in 30 laryngeal carcinoma tissues and cell lines (TU212, TU686, and AMC-HN-8). Notably, TFRC and FTH1 expression levels were significantly associated with patient prognosis. Conclusion Three prognostic genes, TFRC, RGS4, and FTH1, were identified as m6A-regulated ferroptosis biomarkers in LC, providing insights into LC treatment and prognosis.

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