Reclassification of endometrial cancer and identification of key genes based on neural-related genes

Fan, Chenyi; Qin, Tiansheng; Zhang, Yigan; Wei, Linzhen; Dang, Yanan; Liu, Peixia; Jin, Wei‐Lin

doi:10.3389/fonc.2022.951437

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…Previous studies have shown that the interaction between neuroactive ligands and receptors, as a part of the TME, affects tumor progression and invasion ( 68 ). Although there are no clear studies showing that nerve signaling can affect the immune microenvironment of patients with EC, the role of neuro-related genes in the reclassification of EC has been previously suggested ( 69 ). These findings indicated that neuroactive ligand receptor interactions in the TME may be used as a new research direction for immunotherapy of EC in the future.…”

Section: Discussionmentioning

confidence: 99%

ZAP70 interaction with 13 mRNAs as a potential immunotherapeutic target for endometrial cancer

Zhang

2023

Oncol Lett

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For advanced, refractory endometrial cancer (EC), it is advisable to find effective immunotherapeutic targets. In the present study, genes affecting the immune status of uterine corpus endometrial carcinoma (UCEC) samples within The Cancer Genome Atlas were explored by weighted correlation network analysis and differential gene expression analysis. The protein function and immune correlation of 14 key genes, including ζ-chain-associated protein kinase 70 (ZAP70), were analyzed. Based on the expression levels of key genes, the patients with UCEC were divided into two groups using consensus clustering, low expression (group 1) and high expression (group 2). Next, the functions of differentially expressed genes (DEGs) between the two groups were identified using Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes analysis and Gene Set Enrichment Analysis. The immune status of the patients in the two groups was evaluated using immune infiltration score and the expression levels of targets of immune checkpoint inhibitors. The role of ZAP70 in the prognosis of patients with UCEC and the differences in ZAP70 expression between EC tissues and healthy intimal tissues were determined by reverse transcription-quantitative PCR and immunohistochemistry. The present study found strong correlations between key genes, including ZAP70,

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Section: Discussionmentioning

confidence: 99%

ZAP70 interaction with 13 mRNAs as a potential immunotherapeutic target for endometrial cancer

Zhang

2023

Oncol Lett

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“…Sanders et al (2022) [63] also found the expression of EZH2 to be highly variable across their identified spatial clusters. Finally, from the set (d), SEMA4F has been found to be associated with endometrial cancer [68].…”

Section: Plos Geneticsmentioning

confidence: 99%

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

Seal,

Bitler,

Ghosh

2023

PLoS Genet

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In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights.

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“…Sanders et al (2022) [54] also found the expression of EZH2 to be highly variable across their identified spatial clusters. Finally, from the set (d), SEMA4F has been found to be associated with endometrial cancer [59].…”

Section: Sccoht By 10x Visiummentioning

confidence: 99%

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

Seal

Bitler

Ghosh

2023

Preprint

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In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can provide crucial biological insights into both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.

show abstract

Reclassification of endometrial cancer and identification of key genes based on neural-related genes

Cited by 4 publications

References 43 publications

ZAP70 interaction with 13 mRNAs as a potential immunotherapeutic target for endometrial cancer

ZAP70 interaction with 13 mRNAs as a potential immunotherapeutic target for endometrial cancer

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

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