Comprehensive analysis of ZNF family genes in prognosis, immunity, and treatment of esophageal cancer

列线图 比例危险模型 肿瘤科 医学 内科学 接收机工作特性 生存分析 单变量 多元分析 单变量分析 外科肿瘤学 多元统计 数学 统计
作者
Kunqiao Hong,Qian Yang,Haisen Yin,Na Wei,Wei Wang,Baoping Yu
出处
期刊:BMC Cancer [Springer Nature]
卷期号:23 (1) 被引量:7
标识
DOI:10.1186/s12885-023-10779-5
摘要

As a common malignant tumor, esophageal carcinoma (ESCA) has a low early diagnosis rate and poor prognosis. This study aimed to construct the prognostic features composed of ZNF family genes to effectively predict the prognosis of ESCA patients.The mRNA expression matrix and clinical data were downloaded from TCGA and GEO database. Using univariate Cox analysis, lasso regression and multivariate Cox analysis, we screened six prognosis-related ZNF family genes to construct the prognostic model. We then used Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, and nomogram to evaluate the prognostic value within and across sets, separately and combined. We also validated the prognostic value of the six-gene signature using GSE53624 dataset. The different immune status was observed in the single sample Gene Set Enrichment Analysis (ssGSEA). Finally, real-time quantitative PCR was used to detect the expression of six prognostic ZNF genes in twelve pairs of ESCA and adjacent normal tissues.A six prognosis-related ZNF family genes model consisted of ZNF91, ZNF586, ZNF502, ZNF865, ZNF106 and ZNF225 was identified. Multivariable Cox regression analysis revealed that six prognosis-related ZNF family genes were independent prognostic factors for overall survival of ESCA patients in TCGA and GSE53624. Further, a prognostic nomogram including the riskScore, age, gender, T, stage was constructed, and TCGA/GSE53624-based calibration plots indicated its excellent predictive performance. Drug Sensitivity and ssGSEA analysis showed that the six genes model was closely related to immune cells infiltration and could be used as a potential predictor of chemotherapy sensitivity.We identified six prognosis-related ZNF family genes model of ESCA, which provide evidence for individualized prevention and treatment.

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
1秒前
2秒前
zh完成签到,获得积分10
3秒前
善学以致用应助大鱼采纳,获得10
3秒前
hmr关注了科研通微信公众号
4秒前
4秒前
勤劳的乐安完成签到,获得积分10
5秒前
lz4540发布了新的文献求助10
6秒前
情怀应助DamonFri采纳,获得10
6秒前
6秒前
所所应助吃掉记忆面包采纳,获得10
6秒前
陈开心发布了新的文献求助10
7秒前
丘比特应助干净冷亦采纳,获得50
7秒前
玉暖阳发布了新的文献求助10
8秒前
8秒前
传奇3应助小小鹿采纳,获得10
10秒前
跳跃的煎饼完成签到 ,获得积分10
11秒前
winnerbing发布了新的文献求助10
11秒前
嘒彼星发布了新的文献求助10
11秒前
英俊的铭应助zh采纳,获得10
14秒前
15秒前
15秒前
调皮元珊发布了新的文献求助10
15秒前
Future完成签到 ,获得积分10
16秒前
16秒前
liuderui发布了新的文献求助10
16秒前
17秒前
搞怪曼波完成签到 ,获得积分10
17秒前
Ellalala完成签到 ,获得积分10
18秒前
20秒前
仁爱吐司发布了新的文献求助10
21秒前
大熊发布了新的文献求助10
21秒前
活力的泥猴桃完成签到 ,获得积分10
22秒前
22秒前
24秒前
24秒前
lyt发布了新的文献求助10
24秒前
bji发布了新的文献求助10
26秒前
zzx完成签到 ,获得积分10
27秒前
27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
Kinesiophobia : a new view of chronic pain behavior 3000
Les Mantodea de guyane 2500
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
Brittle Fracture in Welded Ships 500
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5941820
求助须知:如何正确求助?哪些是违规求助? 7064711
关于积分的说明 15886673
捐赠科研通 5072199
什么是DOI,文献DOI怎么找? 2728359
邀请新用户注册赠送积分活动 1686934
关于科研通互助平台的介绍 1613254