Suppr超能文献

长链非编码 RNA ALMS1-IT1 是结直肠腺癌的一种新型预后生物标志物,并与免疫浸润相关。

LncRNA ALMS1-IT1 is a novel prognostic biomarker and correlated with immune infiltrates in colon adenocarcinoma.

机构信息

Xiamen Key Laboratory of Biomarker Translational Medicine, Medical Laboratory of Xiamen Humanity Hospital Fujian Medical University, Xiamen, China.

Ultrasonography Department, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China.

出版信息

Medicine (Baltimore). 2022 Oct 21;101(42):e31314. doi: 10.1097/MD.0000000000031314.

Abstract

Colon adenocarcinoma (COAD) is one of the most serious cancers. It is important to accurately predict prognosis and provide individualized treatment. Evidence suggests that clinicopathological features and immune status of the body are related to the occurrence and development of cancer. Expression of long non-coding RNA (LncRNA) ALMS1 intronic transcript 1 (ALMS1-IT1) is observed in some cancer types, and we believe that it may have the potential to serve as a marker of COAD. Therefore, we used the data obtained from the cancer genome atlas (TCGA) database to prove the relationship between ALMS1-IT1 and COAD. Wilcoxon rank sum test, Chi-square test, Fisher exact test and logistic regression were used to evaluate relationships between clinical-pathologic features and ALMS1-IT1 expression. Receiver operating characteristic curves were used to describe binary classifier value of ALMS1-IT1 using area under curve score. Kaplan-Meier method and Cox regression analysis were used to evaluate factors contributing to prognosis. Gene oncology (GO) and (Kyoto Encyclopedia of Genes and Genomes) KEGG enrichment analysis were used to predict the function of differentially expressed genes associated with ALMS1-IT1. Gene set enrichment analysis (GSEA) was used to predict canonical pathways associated with ALMS1-IT1.Immune infiltration analysis was performed to identify the significantly involved functions of ALMS1-IT1. Starbase database was used to predict miRNAs and RNA binding proteins (RBPs) that may interact with ALMS1-IT1. Increased ALMS1-IT1 expression in COAD was associated with N stage (P < .001), M stage (P = .003), Pathologic stage (P = .002), and Primary therapy outcome (P = .009). Receiver operating characteristic curve suggested the significant diagnostic and prognostic ability of ALMS1-IT1 (area under curve = 0.857). High ALMS1-IT1 expression predicted a poorer overall-survival (P = .005) and poorer progression-free interval (PFI) (P = .012), and ALMS1-IT1 expression was independently correlated with PFI in COAD patients (hazard ratio (HR) :1.468; 95% CI: 1.029-2.093; P =.034) (HR: 1.468; 95% CI: 1.029-2.093; P = .034). GO, KEGG, GSEA, and immune infiltration analysis showed that ALMS1-IT1 expression was correlated with regulating the function of DNA and some types of immune infiltrating cells. ALMS1-IT1 expression was significantly correlated with poor survival and immune infiltrations in COAD, and it may be a promising prognostic biomarker in COAD.

摘要

结直肠腺癌 (COAD) 是最严重的癌症之一。准确预测预后并提供个体化治疗非常重要。有证据表明,临床病理特征和机体的免疫状态与癌症的发生和发展有关。长链非编码 RNA (LncRNA) ALMS1 内含子转录本 1 (ALMS1-IT1) 在一些癌症类型中表达,我们认为它可能具有作为 COAD 标志物的潜力。因此,我们使用从癌症基因组图谱 (TCGA) 数据库中获得的数据来证明 ALMS1-IT1 与 COAD 之间的关系。Wilcoxon 秩和检验、卡方检验、Fisher 确切检验和逻辑回归用于评估临床病理特征与 ALMS1-IT1 表达之间的关系。使用曲线下面积评分描述 ALMS1-IT1 的二分类器值的接收者操作特征曲线。Kaplan-Meier 法和 Cox 回归分析用于评估与预后相关的因素。基因肿瘤学 (GO) 和京都基因与基因组百科全书 (KEGG) 富集分析用于预测与 ALMS1-IT1 相关的差异表达基因的功能。基因集富集分析 (GSEA) 用于预测与 ALMS1-IT1 相关的典型途径。免疫浸润分析用于确定 ALMS1-IT1 显著涉及的功能。Starbase 数据库用于预测可能与 ALMS1-IT1 相互作用的 miRNA 和 RNA 结合蛋白 (RBP)。COAD 中 ALMS1-IT1 表达增加与 N 期 (P<0.001)、M 期 (P=0.003)、病理分期 (P=0.002) 和原发性治疗结果 (P=0.009) 相关。接收者操作特征曲线表明 ALMS1-IT1 具有显著的诊断和预后能力 (曲线下面积=0.857)。高 ALMS1-IT1 表达预测总生存期较差 (P=0.005) 和无进展间隔 (PFI) 较差 (P=0.012),并且 ALMS1-IT1 表达在 COAD 患者中与 PFI 独立相关 (风险比 (HR):1.468;95% CI:1.029-2.093;P=0.034) (HR:1.468;95% CI:1.029-2.093;P=0.034)。GO、KEGG、GSEA 和免疫浸润分析表明,ALMS1-IT1 表达与调节 DNA 功能和某些类型的免疫浸润细胞有关。ALMS1-IT1 表达与 COAD 中的不良生存和免疫浸润显著相关,它可能是 COAD 中一种有前途的预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/693f/9592486/78f42b81d719/medi-101-e31314-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验