Transcriptome analysis of adipocytokines and their-related LncRNAs in lung adenocarcinoma revealing the association with prognosis, immune infiltration, and metabolic characteristics.
Transcriptome analysis of adipocytokines and their-related LncRNAs in lung adenocarcinoma revealing the association with prognosis, immune infiltration, and metabolic characteristics.
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DOI：
10.1080/21623945.2022.2064956
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发表时间：
2022-12
期刊：
影响因子：
3.3
通讯作者：
中科院分区：
文献类型：
--
作者：
肺腺癌（LUAD）是全球范围内癌症相关死亡的主要原因之一。脂肪细胞因子和长链非编码RNA（lncRNA）是癌症中不可或缺的参与者。我们对脂肪细胞因子的 mRNA 表达、单核苷酸变异、拷贝数变异和预后价值进行了泛癌分析。 LUAD 样本来自基因表达综合库 (GEO) 和癌症基因组图谱 (TCGA) 数据库。同时，对训练组、内部组和外部组进行分组。通过最小绝对收缩和选择算子回归分析、随机森林算法和 Cox 回归分析逐步筛选优化基因后，构建了与另外四个成熟的生存预测特征相比具有优越性能的脂肪细胞因子相关预后特征 (ARPS)。在确定风险水平后，通过多种生物信息学方法探讨低风险和高风险人群中免疫微环境、免疫检查点基因表达、免疫亚型和免疫反应的差异。通过基因集富集分析（GSEA）确定了高风险和低风险亚组的异常途径。通过单样本 GSEA 选择与风险评分相关的免疫和代谢相关途径。最后，绘制了具有满意的预测生存概率的列线图。总之，本研究为临床治疗和科学研究提供了有意义的信息。
Lung adenocarcinoma (LUAD) is amongst the major contributors to cancer-related deaths on a global scale. Adipocytokines and long non-coding RNAs (lncRNAs) are indispensable participants in cancer. We performed a pan-cancer analysis of the mRNA expression, single nucleotide variation, copy number variation, and prognostic value of adipocytokines. LUAD samples were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Simultaneously, train, internal and external cohorts were grouped. After a stepwise screening of optimized genes through least absolute shrinkage and selection operator regression analysis, random forest algorithm,, and Cox regression analysis, an adipocytokine-related prognostic signature (ARPS) with superior performance compared with four additional well-established signatures for survival prediction was constructed. After determination of risk levels, the discrepancy of immune microenvironment, immune checkpoint gene expression, immune subtypes, and immune response in low- and high-risk cohorts were explored through multiple bioinformatics methods. Abnormal pathways underlying high- and low-risk subgroups were identified through gene set enrichment analysis (GSEA). Immune-and metabolism-related pathways that were correlated with risk score were selected through single sample GSEA. Finally, a nomogram with satisfied predictive survival probability was plotted. In summary, this study offers meaningful information for clinical treatment and scientific investigation.
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影响因子：
14.9
作者：
Howe KL;Achuthan P;Allen J;Allen J;Alvarez-Jarreta J;Amode MR;Armean IM;Azov AG;Bennett R;Bhai J;Billis K;Boddu S;Charkhchi M;Cummins C;Da Rin Fioretto L;Davidson C;Dodiya K;El Houdaigui B;Fatima R;Gall A;Garcia Giron C;Grego T;Guijarro-Clarke C;Haggerty L;Hemrom A;Hourlier T;Izuogu OG;Juettemann T;Kaikala V;Kay M;Lavidas I;Le T;Lemos D;Gonzalez Martinez J;Marugán JC;Maurel T;McMahon AC;Mohanan S;Moore B;Muffato M;Oheh DN;Paraschas D;Parker A;Parton A;Prosovetskaia I;Sakthivel MP;Salam AIA;Schmitt BM;Schuilenburg H;Sheppard D;Steed E;Szpak M;Szuba M;Taylor K;Thormann A;Threadgold G;Walts B;Winterbottom A;Chakiachvili M;Chaubal A;De Silva N;Flint B;Frankish A;Hunt SE;IIsley GR;Langridge N;Loveland JE;Martin FJ;Mudge JM;Morales J;Perry E;Ruffier M;Tate J;Thybert D;Trevanion SJ;Cunningham F;Yates AD;Zerbino DR;Flicek P
通讯作者：
Flicek P
DOI：
10.1530/joe-13-0339
发表时间：
2014-02
期刊：
The Journal of endocrinology
影响因子：
--
作者：
Cao H
通讯作者：
Cao H
影响因子：
8
作者：
Li, D.;Beisswenger, C.;Bals, R.
通讯作者：
Bals, R.
影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
影响因子：
7.4
作者：
Guo, Dina;Wang, Mian;Zhu, Jiaona
通讯作者：
Zhu, Jiaona