Anoikis-related genes as potential prognostic biomarkers in gastric cancer: A multilevel integrative analysis and predictive therapeutic value

IF 1.9 4区生物学 Q4 CELL BIOLOGY

IET Systems Biology Pub Date : 2024-02-20 DOI:10.1049/syb2.12088

Yongjian Lin, Jinlu Liu

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引用次数: 0

Abstract

Background

Gastric cancer (GC) is a frequent malignancy of the gastrointestinal tract. Exploring the potential anoikis mechanisms and pathways might facilitate GC research.

Purpose

The authors aim to determine the significance of anoikis-related genes (ARGs) in GC prognosis and explore the regulatory mechanisms in epigenetics.

Methods

After describing the genetic and transcriptional alterations of ARGs, we searched differentially expressed genes (DEGs) from the cancer genome atlas and gene expression omnibus databases to identify major cancer marker pathways. The non-negative matrix factorisation algorithm, Lasso, and Cox regression analysis were used to construct a risk model, and we validated and assessed the nomogram. Based on multiple levels and online platforms, this research evaluated the regulatory relationship of ARGs with GC.

Results

Overexpression of ARGs is associated with poor prognosis, which modulates immune signalling and promotes anti-anoikis. The consistency of the DEGs clustering with weighted gene co-expression network analysis results and the nomogram containing 10 variable genes improved the clinical applicability of ARGs. In anti-anoikis mode, cytology, histology, and epigenetics could facilitate the analysis of immunophenotypes, tumour immune microenvironment (TIME), and treatment prognosis.

Conclusion

A novel anoikis-related prognostic model for GC is constructed, and the significance of anoikis-related prognostic genes in the TIME and the metabolic pathways of tumours is initially explored.

Abstract Image

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作为胃癌潜在预后生物标志物的Anoikis相关基因：多层次综合分析和预测治疗价值。

背景：胃癌（GC）是一种常见的消化道恶性肿瘤。目的：作者旨在确定厌氧相关基因（ARGs）在胃癌预后中的意义，并探索表观遗传学中的调控机制：在描述了ARGs的遗传和转录改变后，我们从癌症基因组图谱和基因表达综合数据库中搜索了差异表达基因（DEGs），以确定主要的癌症标记通路。我们利用非负矩阵因式分解算法、Lasso 和 Cox 回归分析构建了风险模型，并对提名图进行了验证和评估。基于多层次和在线平台，该研究评估了ARGs与GC的调控关系：结果：ARGs的过表达与预后不良有关，它能调节免疫信号并促进抗瘤。DEGs聚类与加权基因共表达网络分析结果以及包含10个可变基因的提名图的一致性提高了ARGs的临床适用性。在抗瘤模式中，细胞学、组织学和表观遗传学有助于分析免疫表型、肿瘤免疫微环境（TIME）和治疗预后：结论：本文构建了一个新的与肿瘤免疫相关的GC预后模型，并初步探讨了与肿瘤免疫相关的预后基因在肿瘤免疫微环境（TIME）和代谢途径中的意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IET Systems Biology 生物-数学与计算生物学

CiteScore

4.20

自引率

4.30%

发文量

审稿时长

>12 weeks

期刊介绍： IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.