Bacterial lipopolysaccharide related genes signature as potential biomarker for prognosis and immune treatment in gastric cancer

免疫疗法 医学 生物标志物 癌症 肿瘤科 基因签名 内科学 基因 计算生物学 生物信息学 生物 遗传学 基因表达
作者
Tianyi Yuan,Siming Zhang,Songnian He,Yijie Ma,Jianhong Chen,Jue Gu
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:13 (1) 被引量:3
标识
DOI:10.1038/s41598-023-43223-6
摘要

Abstract The composition of microbial microenvironment is an important factor affecting the development of tumor diseases. However, due to the limitations of current technological levels, we are still unable to fully study and elucidate the depth and breadth of the impact of microorganisms on tumors, especially whether microorganisms have an impact on cancer. Therefore, the purpose of this study is to conduct in-depth research on the role and mechanism of prostate microbiome in gastric cancer (GC) based on the related genes of bacterial lipopolysaccharide (LPS) by using bioinformatics methods. Through comparison in the Toxin Genomics Database (CTD), we can find and screen out the bacterial LPS related genes. In the study, Venn plots and lasso analysis were used to obtain differentially expressed LPS related hub genes (LRHG). Afterwards, in order to establish a prognostic risk score model and column chart in LRHG features, we used univariate and multivariate Cox regression analysis for modeling and composition. In addition, we also conducted in-depth research on the clinical role of immunotherapy with TMB, MSI, KRAS mutants, and TIDE scores. We screened 9 LRHGs in the database. We constructed a prognostic risk score and column chart based on LRHG, indicating that low risk scores have a protective effect on patients. We particularly found that low risk scores are beneficial for immunotherapy through TIDE score evaluation. Based on LPS related hub genes, we established a LRHG signature, which can help predict immunotherapy and prognosis for GC patients. Bacterial lipopolysaccharide related genes can also be biomarkers to predict progression free survival in GC patients.
最长约 10秒,即可获得该文献文件

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
Sean发布了新的文献求助10
1秒前
JamesPei应助章慕思采纳,获得10
1秒前
小杨爱晒太阳完成签到,获得积分10
1秒前
感性的穆完成签到,获得积分10
2秒前
wzzznh发布了新的文献求助10
2秒前
好好好完成签到 ,获得积分10
3秒前
XCXC应助你好采纳,获得10
4秒前
科研通AI5应助务实可乐采纳,获得10
5秒前
Ashes应助zhou采纳,获得30
6秒前
6秒前
小玉应助Sean采纳,获得10
8秒前
9秒前
lois完成签到,获得积分20
10秒前
科研不通发布了新的文献求助10
10秒前
欣喜谷槐完成签到,获得积分10
10秒前
嘘嘘完成签到,获得积分10
11秒前
jiujiuwo完成签到,获得积分10
12秒前
开朗冬萱完成签到 ,获得积分10
12秒前
牛牛要当院士喽完成签到,获得积分10
13秒前
starts发布了新的文献求助10
14秒前
汉堡包应助Fine采纳,获得10
14秒前
合适不愁完成签到,获得积分10
15秒前
15秒前
Sean完成签到,获得积分10
16秒前
shengwang完成签到,获得积分10
16秒前
科研通AI5应助heli采纳,获得10
17秒前
18秒前
慕青应助allofme采纳,获得10
18秒前
19秒前
牛牛要当院士喽完成签到,获得积分10
20秒前
21秒前
starts完成签到,获得积分10
21秒前
科研通AI5应助zz采纳,获得10
21秒前
21秒前
Xiaopei发布了新的文献求助10
22秒前
23秒前
24秒前
Fine发布了新的文献求助10
25秒前
galioo3000发布了新的文献求助10
25秒前
研友_Z7WQzZ发布了新的文献求助20
26秒前
高分求助中
Mass producing individuality 600
Algorithmic Mathematics in Machine Learning 500
Разработка метода ускоренного контроля качества электрохромных устройств 500
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
Advances in Underwater Acoustics, Structural Acoustics, and Computational Methodologies 300
Worked Bone, Antler, Ivory, and Keratinous Materials 200
The Physical Oceanography of the Arctic Mediterranean Sea 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3828040
求助须知:如何正确求助?哪些是违规求助? 3370323
关于积分的说明 10462906
捐赠科研通 3090294
什么是DOI,文献DOI怎么找? 1700312
邀请新用户注册赠送积分活动 817813
科研通“疑难数据库(出版商)”最低求助积分说明 770458