High expression of PLAC1 in colon cancer as a predictor of poor prognosis: A study based on TCGA data

结直肠癌 内科学 癌症研究 生物 癌症 肿瘤科 医学 遗传学
作者
Yu Ren,Yunxia Lv,Taiyuan Li,Qunguang Jiang
出处
期刊:Gene [Elsevier BV]
卷期号:763: 145072-145072 被引量:15
标识
DOI:10.1016/j.gene.2020.145072
摘要

Colon cancer is one of the most common diseases in the world with both a high incidence and high mortality. PLAC1 is activated and expressed in many cancers. We aim to explore the relationship between PLAC1 expression and prognosis in colon cancer patient. The RNA-Seq expression data and clinical information of colon cancer were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed PLAC1 was obtained by the Wilcoxon signed-rank test; the significance difference being that PLAC1 was more highly expressed in tumor rather than normal tissue (p < 0.01). Then patients were classified into high and low risk groups by different risk scores, and the Kaplan–Meier survival analysis showed that colon cancer patients with a high PLAC1 expression had a poorer prognosis than low PLAC1 expression patients (p = 0.0031). Next, in analyzing the clinical pathology associated with PLAC1 expression, logistic regression showed that PLAC1 was expressed high in stage (OR = 4.11 for I vs. IV), lymph nodes (OR = 1.73 for N0 vs. N1+), distant metastasis (OR = 2.8 for M0 vs. M1), and status (OR = 22.81 for normal vs. tumor). Univariate and multivariate cox analyses were employed to identify that PLAC1 could be regarded as an independent prognostic factor. Univariate cox analysis showed PLAC1 had a correlation to overall survival (OS) (HR: 0.46, 95% CI: 0.28–0.77, p = 0.003). Multivariate cox analysis revealed that PLAC1 (HR: 0.51, 95% CI: 0.30–0.86, p = 0.012) could be regarded as an Independent prognostic factor. We also used quantitative real-time polymerase chain reaction (qRT-PCR) to test if PLAC1 was differently expressed in cell lines. The qRT-PCR obtained the significant results that PLAC1 expressed high in colon cancer cell lines (p < 0.05). Finally, Gene Set Enrichment Analysis (GSEA) was utilized to show 14 enriched signaling pathways. Our study discovered that high expression of PLAC1 predicts poor prognosis in colon cancer patients, providing a new biomarker, which can assist physicians in finding new diagnostic and therapy methods for colon cancer.
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
刚刚
充电宝应助光亮的依凝采纳,获得10
1秒前
汉小弟完成签到,获得积分10
2秒前
kkkkkoi发布了新的文献求助10
2秒前
Du发布了新的文献求助10
2秒前
3秒前
CT发布了新的文献求助10
3秒前
Hello应助lcsw采纳,获得10
4秒前
meimale发布了新的文献求助10
4秒前
guanyc发布了新的文献求助10
4秒前
雾色笼晓树苍完成签到 ,获得积分10
5秒前
亦安发布了新的文献求助30
5秒前
7秒前
Akim应助机智的大狸子采纳,获得10
7秒前
Glu发布了新的文献求助10
7秒前
打打应助kkkkkoi采纳,获得10
10秒前
abc123发布了新的文献求助10
10秒前
drleslie完成签到,获得积分10
11秒前
光亮的依凝完成签到,获得积分10
11秒前
Okeah完成签到,获得积分10
13秒前
初景发布了新的文献求助10
13秒前
满意血茗完成签到,获得积分10
14秒前
靓丽的悒完成签到 ,获得积分10
14秒前
15秒前
15秒前
15秒前
深情安青应助畅快代玉采纳,获得10
16秒前
爆米花应助一个达不刘采纳,获得10
16秒前
17秒前
17秒前
烟花应助科研通管家采纳,获得10
18秒前
18秒前
wanci应助科研通管家采纳,获得10
18秒前
英俊的铭应助科研通管家采纳,获得10
18秒前
arniu2008应助科研通管家采纳,获得40
18秒前
18秒前
慕青应助科研通管家采纳,获得10
18秒前
18秒前
18秒前
bkagyin应助科研通管家采纳,获得10
19秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(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
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
CLSI M07 2024 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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7249327
求助须知:如何正确求助?哪些是违规求助? 8872044
关于积分的说明 18721028
捐赠科研通 6928579
什么是DOI,文献DOI怎么找? 3198695
关于科研通互助平台的介绍 2374012
邀请新用户注册赠送积分活动 2173284