Telomere length-related signature as a novel biomarker of prognosis and immune response in non-small cell lung cancer.

肿瘤科 比例危险模型 肺癌 医学 内科学 接收机工作特性 生存分析 生物标志物 免疫系统 多元分析 免疫学 生物 生物化学
作者
Liu Xg,M Li,S-J Mai,R-J Cai
出处
期刊:DOAJ: Directory of Open Access Journals - DOAJ 卷期号:26 (4): 1304-1319 被引量:6
标识
DOI:10.26355/eurrev_202202_28124
摘要

Telomere length-related genes (TLRGs) play an important role in multiple tumors; however, there is a lack of systematic reporting about their relevance in non-small cell lung cancer (NSCLC). This study investigated the relation between TLRG gene expression and the immunotherapeutic response of patients with NSCLC.Differentially expressed TLRGs in tumor tissues and normal tissues were screened using Gene Expression Omnibus (GEO) datasets. A univariate Cox regression analysis was performed to identify the optimal prognosis-related genes. A prognostic risk model was constructed by using least absolute shrinkage, selection operator, and multivariate Cox regression analysis results. The model was then evaluated by a Kaplan-Meier analysis, functional enrichment annotation, and a receiver operating characteristic curve analysis; after which, it was validated in the TCGA dataset. The model was used to predict immunotherapeutic response and drug sensitivity.An 18-gene prognostic signature was developed and used to stratify NSCLC patients into a low- or high-risk group in GEO cohorts. Patients in the low-risk group had better survival possibilities than those in the high-risk group, and showed significantly higher overall survival times in the TCGA cohort. The risk score was identified as an independent prognostic factor, when compared with other clinical factors. ssGSEA scores showed that the risk model was mainly linked to cancer- and immune-related pathways. Importantly, the candidate risk model was linked to tumor immunity and predicted a patient's response to PDL-1 blockade immune therapy. Several potential drugs that might target this model were identified.This study provides broad molecular signatures that can be used in further functional and therapeutic studies of the telomere system, and also represents an integrated approach for characterizing key protein complexes when creating a prognosis and identifying new targets for cancer immunotherapy.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
钟123完成签到,获得积分10
刚刚
1秒前
lian发布了新的文献求助10
1秒前
Vega完成签到,获得积分10
1秒前
记得吃早饭完成签到 ,获得积分10
2秒前
共享精神应助初景采纳,获得10
2秒前
高兴的平露完成签到,获得积分10
2秒前
3秒前
3秒前
钟123发布了新的文献求助10
3秒前
皮皮鲁完成签到,获得积分10
4秒前
4秒前
6秒前
6秒前
1259671587完成签到,获得积分10
6秒前
lihouying关注了科研通微信公众号
6秒前
传奇3应助李某人采纳,获得10
7秒前
锂离子发布了新的文献求助10
7秒前
路哈哈完成签到,获得积分10
7秒前
lihouying关注了科研通微信公众号
7秒前
Maggie关注了科研通微信公众号
7秒前
7秒前
8秒前
lee0708发布了新的文献求助10
8秒前
小雨应助活力白竹采纳,获得10
8秒前
long4jun3发布了新的文献求助10
8秒前
8秒前
zhoumu发布了新的文献求助10
9秒前
Biubiubiu完成签到,获得积分10
9秒前
9秒前
9秒前
冰冰发布了新的文献求助10
10秒前
科研通AI6.3应助钟123采纳,获得10
10秒前
zeng发布了新的文献求助10
10秒前
韶芸遥完成签到,获得积分10
10秒前
zhuyuntao发布了新的文献求助10
10秒前
柠可完成签到,获得积分10
11秒前
11秒前
liu完成签到,获得积分10
12秒前
科研乞丐完成签到,获得积分10
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Organometallic Chemistry of the Transition Metals 800
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6441398
求助须知:如何正确求助?哪些是违规求助? 8255357
关于积分的说明 17576780
捐赠科研通 5500021
什么是DOI,文献DOI怎么找? 2900183
邀请新用户注册赠送积分活动 1877028
关于科研通互助平台的介绍 1717044