Identification of Vesicle-Mediated Transport-Related Genes for Predicting Prognosis, Immunotherapy Response, and Drug Screening in Cervical Cancer

IF 3.1 4区 医学 Q3 IMMUNOLOGY
Shuai Lou, Hongqing Lv, Lin Zhang
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引用次数: 0

Abstract

Background

Cervical cancer is one of the most common malignancies among women. Vesicle-mediated transport mechanisms significantly influence tumor cell behavior through intercellular material exchange. However, prognostic significance in CC patients remains underexplored.

Research Design and Methods

We identified differentially expressed vesicle-mediated transport-related genes from TCGA and GeneCards datasets through differential expression analysis. We constructed a prognostic model using Cox regression and LASSO regression, categorized patients into high- and low-risk groups, and validated the model in the GEO data set. A nomogram integrating clinical features and risk scores demonstrated the model's independent prognostic capability. We analyzed tumor immune cell infiltration, immune checkpoints, and predicted immunotherapy responses in the high- and low-risk groups. Finally, we screened potential drugs for targeting CC and conducted drug-sensitivity analysis.

Results

We successfully established a 10-gene prognostic model based on VMTRGs. The low-risk group exhibited favorable prognosis, significant immune cell infiltration, and promising immunotherapy response, whereas the high-risk group showed higher sensitivity to chemotherapeutic agents such as Docetaxel and Paclitaxel. Potential drugs identified for targeting CC patients included Megestrol acetate, Lenvatinib, Adavosertib, and Barasertib.

Conclusions

The VMTRG-based prognostic model demonstrates reliable clinical prognostic value and enhances understanding of vesicle-mediated transport mechanisms in CC.

Abstract Image

鉴定用于预测宫颈癌预后、免疫疗法反应和药物筛选的囊泡介导转运相关基因
背景:宫颈癌是女性最常见的恶性肿瘤之一:宫颈癌是女性最常见的恶性肿瘤之一。囊泡介导的运输机制通过细胞间的物质交换对肿瘤细胞的行为产生重大影响。然而,宫颈癌患者的预后意义仍未得到充分探讨:我们通过差异表达分析从TCGA和GeneCards数据集中识别了差异表达的囊泡介导转运相关基因。我们利用 Cox 回归和 LASSO 回归构建了一个预后模型,将患者分为高风险组和低风险组,并在 GEO 数据集中验证了该模型。一个整合了临床特征和风险评分的提名图证明了该模型的独立预后能力。我们分析了肿瘤免疫细胞浸润、免疫检查点,并预测了高风险组和低风险组的免疫治疗反应。最后,我们筛选了针对CC的潜在药物,并进行了药物敏感性分析:结果:我们成功建立了基于VMTRGs的10基因预后模型。低危组预后良好,免疫细胞浸润明显,免疫治疗反应良好,而高危组对多西他赛和紫杉醇等化疗药物的敏感性较高。针对CC患者的潜在药物包括醋酸甲地孕酮、Lenvatinib、Adavosertib和Barasertib:基于VMTRG的预后模型具有可靠的临床预后价值,并加深了人们对CC中囊泡介导的转运机制的了解。
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来源期刊
Immunity, Inflammation and Disease
Immunity, Inflammation and Disease Medicine-Immunology and Allergy
CiteScore
3.60
自引率
0.00%
发文量
146
审稿时长
8 weeks
期刊介绍: Immunity, Inflammation and Disease is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research across the broad field of immunology. Immunity, Inflammation and Disease gives rapid consideration to papers in all areas of clinical and basic research. The journal is indexed in Medline and the Science Citation Index Expanded (part of Web of Science), among others. It welcomes original work that enhances the understanding of immunology in areas including: • cellular and molecular immunology • clinical immunology • allergy • immunochemistry • immunogenetics • immune signalling • immune development • imaging • mathematical modelling • autoimmunity • transplantation immunology • cancer immunology
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