Tumor Suppressor 4.1N/EPB41L1 is Epigenetic Silenced by Promoter Methylation and MiR-454-3p in NSCLC

Yang, Qin; Zhu, Lin; Ye, Mao; Zhang, Bin; Zhan, Peihe; Li, Hui; Zhang, Wen; Liu, Jing

doi:10.3389/fgene.2022.805960

Cited by 3 publications

(4 citation statements)

References 53 publications

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“…RT-qPCR was carried out as we previously described [ 7 , 22 , 23 ]. RT-qPCR primers for PDGFRL (forward: 5’GACGACATCAGTGTGCTCTGCA3’, revers: 5’CCAAGTGTCTTGGATCGTCACAG3’).…”

Section: Methodsmentioning

confidence: 99%

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Identification of a unique stress response state of T cells-related gene signature in patients with gastric cancer

Yang,

Li,

Zhu

2024

Aging

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Gastric cancer (GC), the third most lethal cancer worldwide, is often diagnosed at an advanced stage, leaving limited therapeutic options. Given the diverse outcomes among GC patients with similar AJCC/UICC-TNM characteristics, there is a pressing need for more reliable prognostic tools. Recent advances in targeted therapy and immunotherapy have underscored this necessity. In this context, our study focused on a novel stress response state of T cells, termed T STR , identified across multiple cancers, which is associated with resistance to immunotherapy. We aimed to develop a predictive gene signature for the T STR phenotype within the tumor microenvironment (TME) of GC patients. By categorizing GC patients into high and low T STR groups based on the infiltration states of TME T STR cells, we observed significant differences in clinical prognosis and characteristics between the groups. Through a multi-step bioinformatics approach, we established an eight-gene signature based on genes differentially expressed between these groups. We conducted functional validations for the signature gene PDGFRL in GC cells. This gene signature effectively stratifies GC patients into high and low-risk categories, demonstrating robustness in predicting clinical outcomes. Furthermore, these risk groups exhibited distinct immune profiles, somatic mutations, and drug susceptibilities, highlighting the potential of our gene signature to enhance personalized treatment strategies in clinical practice.

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“…RT-qPCR was carried out as we previously described [ 7 , 22 , 23 ]. RT-qPCR primers for PDGFRL (forward: 5’GACGACATCAGTGTGCTCTGCA3’, revers: 5’CCAAGTGTCTTGGATCGTCACAG3’).…”

Section: Methodsmentioning

confidence: 99%

“…MTT, wound healing and transwell experiments were also performed according to our previous protocol [ 7 , 22 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of a unique stress response state of T cells-related gene signature in patients with gastric cancer

Yang,

Li,

Zhu

2024

Aging

Self Cite

View full text Add to dashboard Cite

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“…RT-qPCR primers for β-actin (forward: 5’CATTAAGGAGAAGCTGTGCT3’, revers: 5’GTTGAAGGTAGTTTCGTGGA3’). Western blot were performed according to our previous protocol ( 27 , 28 ).…”

Section: Methodsmentioning

confidence: 99%

“…Cell proliferation assay, wound healing assay and transwell assay were also carried out as we previously described ( 27 , 28 ).…”

Section: Methodsmentioning

confidence: 99%

Subtype classification based on t cell proliferation-related regulator genes and risk model for predicting outcomes of lung adenocarcinoma

2023

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BackgroundLung adenocarcinoma (LUAD), the major lung cancer histotype, represents 40% lung cancers. Currently, outcomes are remarkably different in LUAD patients with similar AJCC/UICC-TNM features. T cell proliferation-related regulator genes (TPRGs) relate to the proliferation, activity and function of T cells and tumor progression. The values of TPRGs in classifying LUAD patients and predicting outcomes remain unknown.MethodsGene expression profile and corresponding clinical data were downloaded from TCGA and the GEO databases. We systematically analyzed the expression profile characteristics of 35 TPRGs in LUAD patients and investigated the differences in overall survival (OS), biology pathway, immunity and somatic mutation between different TPRGs-related subtypes. Subsequently, we constructed a TPRGs-related risk model in TCGA cohort to quantify risk scores using LASSO cox regression analysis and then validated this risk model in two GEO cohorts. LUAD patients were divided into high- and low-risk subtypes according to the median risk score. We systematically compared the biology pathway, immunity, somatic mutation and drug susceptibility between the two risk subtypes. Finally, we validate biological functions of two TPRGs-encoded proteins (DCLRE1B and HOMER1) in LUAD cells A549.ResultsWe identified different TPRGs-related subtypes (including cluster 1/cluster A and its counterpart cluster 2/cluster B). Compared to the cluster 1/cluster A subtype, cluster 2/cluster B subtype tended to have a prominent survival advantage with an immunosuppressive microenvironment and a higher somatic mutation frequency. Then, we constructed a TPRGs-related 6-gene risk model. The high-risk subtype characterized by higher somatic mutation frequency and lower immunotherapy response had a worse prognosis. This risk model was an independent prognostic factor and showed to be reliable and accurate for LUAD classification. Furthermore, subtypes with different risk scores were significantly associated with drug sensitivity. DCLRE1B and HOMER1 suppressed cell proliferation, migration and invasion in LUAD cells A549, which was in line with their prognostic values.ConclusionWe construed a novel stratification model of LUAD based on TPRGs, which can accurately and reliably predict the prognosis and might be used as a predictive tool for LUAD patients.

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LncRNA CALML3-AS1 modulated by m6A modification induces BTNL9 methylation to drive non-small-cell lung cancer progression

Zhang,

Wang,

Zhu

et al. 2023

Cancer Gene Ther

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Tumor Suppressor 4.1N/EPB41L1 is Epigenetic Silenced by Promoter Methylation and MiR-454-3p in NSCLC

Cited by 3 publications

References 53 publications

Identification of a unique stress response state of T cells-related gene signature in patients with gastric cancer

Identification of a unique stress response state of T cells-related gene signature in patients with gastric cancer

Subtype classification based on t cell proliferation-related regulator genes and risk model for predicting outcomes of lung adenocarcinoma

LncRNA CALML3-AS1 modulated by m6A modification induces BTNL9 methylation to drive non-small-cell lung cancer progression

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