急性髓系白血病铁死亡相关基因筛选及预后模型的建立

基于急性髓系白血病(Acute Myeloid Leukemia,AML)临床大数据及多组学数据库探讨铁死亡相关基因在AML中的作用,并建立铁死亡基因表达相关预后模型。整合TCGA数据库中151例AML患者和GTEx数据库中337例正常人外周血的临床和转录组数据。将Wilcoxon检验和单因素Cox分析结果取交集,筛选出预后相关差异表达基因(Differential Expression Genes, DEGs),使用Lasso回归建立基因标志物预后模型,利用受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)评价预测价值,Kaplan-Meier法进行生存分析,对AML患者临床数据进行单因素和多因素Cox回归分析,使用差异基因表达分析等方法比较高、低风险患者间的组学差异,最后,利用BeatAML数据库对基因标志物进行验证。将差异基因表达分析和单因素分析结果取交集,得到13个预后相关DEGs。构建了8个基因标志物的预后评分模型,并将患者分为高、低风险两组;ROC曲线分析证实了模型良好的预测性能;生存分析提示高、低风险组患者的生存率具有显著差异;单因素分析显示年龄和风险评分与患者整体生存显著相关,多因素分析显示,年龄和风险评分是独立预后指标。在2个风险组之间筛选出384个DEGs,GO富集分析结果显示,富集的基因大多与中性粒细胞和白细胞的趋化与迁移等免疫相关分子和通路显著相关,KEGG富集通路主要与TNF信号通路、细胞因子与细胞因子受体相互作用相关。BeatAML数据库验证结果显示,5个基因与预后显著相关。铁死亡相关基因在AML中显著表达,且高风险患者预后较差,该研究对AML铁死亡相关潜在生物标志物的发现和应用奠定了一定的基础。     

一种新的泛素化-铁死亡相关基因特征用于肝细胞癌患者预后分析

本研究通过筛选泛素化-铁死亡相关基因构建肝细胞癌(hepatocellular carcinoma, HCC)预后模型,并探究泛素化-铁死亡相关基因对HCC预后的影响。使用R包DESeq2对TCGA-LIHC数据集进行差异分析,获取HCC差异表达基因(HCC＿DEGs)。将HCC＿DEGs、泛素化相关基因(ubiquitination related genes, URGs)和铁死亡差异表达基因(ferroptosis＿differentially expressed genes, FERR＿EDGs)取交集获得泛素化和铁死亡相关基因(UBFE＿EDGs),将UBFE＿EDGs依次进行LASSO回归分析、单因素Cox回归分析和多因素Cox回归分析以筛选出最佳独立预后基因用于构建HCC预后模型(ubiquitination and ferroptosis-related prognostic model, UBFE)。由生存曲线、 ROC曲线和校正曲线评估UBFE的预测准确性。单因素Cox回归分析、多因素Cox回归分析评估UBFE风险评分是否为HCC的预后独立危险因素,并构建列线图模型。...     

死亡相关的LncRNAs在甲状腺癌中的预后价值及预后风险模型构建

目的:探讨铁死亡相关的长链非编码RNAs(LncRNAs)在甲状腺癌中的预后价值并构建预后风险模型。方法:从癌症基因组图谱(TCGA)数据库下载甲状腺癌的转录本数据和临床数据,铁死亡相关的基因数据集是从铁死亡数据库(http://www.zhounan.org/ferrdb/)下载的259个基因集。得到铁死亡相关LncRNAs,与患者临床信息合并后,通过单因素Cox回归分析和Kaplan-Meier生存分析两种方法得到与甲状腺癌预后相关的铁死亡LncRNAs,通过R的survival包构建COX模型以此来建立最佳预后风险模型并予以验证。结果:获得30个铁死亡相关的LncRNAs,多因素cox分析得到10个与甲状腺癌预后相关的铁死亡LncRNAs,包括AL136366.1、AL162231.2、CRNDE、AC004918.3、LINC02471、AC092279.1、AC046143.1、LINC02454、DOCK9-DT、AC008063.1。Kaplan-Meier生存分析表明高风险组预后较差。单因素和多因素Cox分析表明风险评分可以作为独立预后因子。KEGG通路富集分析发现,差异基因可能与嘧啶代谢、核苷酸切除修复、NOTCH_信号通路等通路有关。结论:通过生物信息学方法筛选出10个与甲状腺癌预后的铁死亡相关LncRNAs,并成功构建预后风险模型。     

肝细胞癌患者自噬相关基因的预后作用

构建由自噬相关基因组成的预后模型,预测肝细胞癌(HCC)患者的生存预后情况,为其个性化诊疗和临床研究提供依据.利用TCGA数据库中HCC的测序信息与人类自噬数据库联合,筛选差异表达的自噬相关基因,对其进行GO富集与KEGG通路分析;通过单因素与多因素Cox分析筛选与患者生存预后明显相关的风险基因,构建预后风险评分模型;...     

内质网应激相关基因能预测骨肉瘤预后并与肿瘤免疫微环境相关

以内质网应激相关基因构建骨肉瘤患者的风险模型,探索其与肿瘤免疫微环境的关系。采用生物信息学分析法,训练集的转录组数据及临床数据下载于UCSC Xena数据库,验证集的相应数据下载于GEO数据库(GSE21257,GSE39058)。采用单因素COX回归分析、LASSO回归分析及多因素COX回归分析提取风险特征基因构建风险模型,使用决策曲线分析、受试者工作特征曲线分析验证模型的准确性,随后构建列线图进一步预测骨肉瘤患者预后;根据风险评分将患者分为高、低风险组,使用Kaplan-Meier生存曲线评估高、低风险组间的生存差异,对差异表达基因(Differentially expressed genes, DEGs)进行GO/KEGG联合富集分析、基因集富集分析(Gene set enrichment analysis, GSEA)及基因集变异分析(Gene set variation analysis, GSVA);采用ESTIMATE算法、微环境种群计数器(Microenvironment cell population counter, MCP counter)方法、单样本基因集富集分析(Single sample gene set enrichment analysis, ssGSEA)进行免疫分析;最终在验证集中验证上述结果。6个风险特征基因中VEGFA、PTGIS及SERPINH1与骨肉瘤患者的不良预后相关,而TMED10、MAPK10及TOR1B与与骨肉瘤患者的良好预后相关,高、低风险组患者之间具有显著生存差异;GO/KEGG联合富集分析、GSVA、GSEA结果表明DEGs与免疫状态相关;免疫分析显示高风险组具有更低的免疫评分及免疫景观;列线图进一步准确地预测了骨肉瘤患者的预后。内质网应激相关基因构建的风险模型能准确预测骨肉瘤患者预后,并与肿瘤免疫微环境相关。     

铁死亡相关基因与结肠癌分型及预后的关系研究

结肠癌是一种常见的发生于结肠部位的消化道恶性肿瘤,它好发于直肠与乙状结肠交界处,恶性程度高,侵袭性强,病情发展快。本研究利用铁死亡相关基因对结肠癌进行分型且对不同分型在生存时间及临床表型方面的差异进行评估,为探索该疾病的发病机制和个性化治疗提供思路。首先,从TCGA-GDC官网下载结肠癌患者的表达数据,通过查找文献检索到60个铁死亡相关基因,筛选有显著差异表达的铁死亡相关基因对结肠癌患者进行无监督聚类分型,同时比较各个分子亚型之间在生存时间和临床特征方面的差异;运用单因素Cox分析法筛选出与预后相关性较高的基因并构建Lasso回归模型,根据回归模型对患者的风险评分将患者分为高风险组和低风险组,比较两组间生存时间的差异并确定风险评分与其他临床特征之间的关联。通过单因素独立预后分析和多因素独立预后分析,筛选出影响结肠癌预后的独立因素。通过无监督聚类将样本分为两种分子亚型,两组间的生存时间差异不显著,不同分子亚型在肿瘤分期这个临床特征间存在一定的差异。使用5个与预后显著相关的基因(FDFT1、HMGCR、CARS1、AKR1C1、ALOX12)构建了Lasso回归模型,根据Lasso回归模型...     

结合自噬相关基因表达谱和临床因素的肺腺癌预后预测模型

对肺腺癌自噬相关基因进行生物信息学分析,结合多基因预后标志和临床参数构建能够预测肺腺癌患者预后的模型。首先,对TCGA肺腺癌数据中的938个自噬相关基因进行差异分析,获得了82个差异自噬相关基因,使用单因素Cox比例风险回归模型从差异自噬相关基因中筛选出候选基因,通过 lasso回归进一步筛选出预后相关基因,分别是ARNTL2、NAPSA、ATG9B、CAPN12、MAP1LC3C和KRT81。通过多因素Cox回归分析以构建风险评分模型,根据最优cutoff值将患者分为高低风险组,生存曲线显示高低风险组之间生存差异显著,ROC曲线显示风险评分的预测能力良好,并在内、外验证集中得到验证。同时对传统的临床因素进行单因素和多因素Cox回归分析,结果显示Stage、复发和风险评分能够独立预测预后,结合这三个独立的预后参数以构建列线图模型,使用一致性指数、校准曲线评估列线图的预测能力,结果显示预测结果与实际结果之间具有良好的一致性。通过与Stage和风险评分的比较发现,列线图的预测能力表现最佳。基于肺腺癌相关的自噬基因和临床参数构建了一个列线图模型来预测肺腺癌患者的预后生存,这可能为临床医生提供了一种可靠的预后评估工具。     

肺腺癌预后相关miRNA生物信息学筛选及其临床意义

目的：探讨肺腺癌预后相关miRNA组学特征及其临床意义。方法：应用癌症基因组图谱（TCGA）数据库,检索人肺腺癌miRNA表达谱数据,进行差异分析,再利用Cox风险回归模型筛选预后相关miRNA;利用mirwalk分析平台,对筛选出的miRNA进行靶向调控基因预测、KEGG功能富集分析,最后,预测出预后相关miRNA的功能。结果：共筛选肺腺癌差异miRNA46个,其中,上调19个、下调27个;通过Cox生存分析筛选到预后相关miRNA有6个,即hsa-mir-21、hsa-mir-142、hsa-mir-200a高表达,hsa-mir-101、hsa-let-7c、hsa-mir-378e低表达,其中hsa-mir-21、hsa-mir-378e与肺腺癌患者不良预后有关,生存期显著缩短（P<0.05,AUC=0.618）。KEGG分析上述预后相关miRNA靶向调控基因与免疫反应通路、miRNA与癌症通路、代谢通路等有关。结论：hsa-mir-21、hsa-mir-378e与肺腺癌预后不良有关,未来经进一步临床验证有可能作为肺腺癌预后相关的分子标记物。     

构建铜死亡相关LncRNA风险模型预测肝细胞癌的预后及分子特征

铜死亡是一种新的程序性细胞死亡途径,由铜与脂酰化三羧酸循环蛋白直接结合而启动。调节肿瘤细胞中的铜死亡是一种新的治疗方法。然而,铜死亡相关长链非编码RNA(LncRNA)在肝细胞癌(HCC)中的潜在作用和临床意义尚不明确。本研究基于TCGA-LIHC数据集对19个铜死亡相关基因进行共表达分析,共鉴定出994个铜死亡相关LncRNA。采用LASSO回归和多因素Cox回归分析筛选出4个与铜死亡相关的预后LncRNA(TMCC1-AS1、AC009974.2、AL355574.1和DDX11-AS1)构建预后风险模型,并计算所有HCC患者样本的风险评分。按1:1的比例将肝癌患者分为高风险组和低风险组。Kaplan-Meier生存曲线分析显示,高风险组患者的总生存率(OS)明显低于低风险组。回归分析和ROC曲线证实了风险评分的预后价值。此外,本研究分析了风险评分与通路富集分析、免疫检查点基因、免疫细胞浸润、抗癌药物敏感性和体细胞基因突变之间的相关性。差异表达分析结果表明,TMCC1-AS1、AC009974.2、AL355574.1和DDX11-AS1在肿瘤组织中的表达均升高。最后,利用收集的8例行根治性手术肝癌患者的癌组织及癌旁肝组织进行实时荧光定量PCR(qRT-PCR)验证,以增加本模型的组织学证据。本研究构建了一个由4种铜死亡相关LncRNA组成的风险模型,该模型与患者的预后及免疫浸润环境明显相关,在预测患者免疫治疗效果及指导化疗药物选择方面具有一定的临床应用价值。     

铜死亡相关LncRNA预测胃癌的预后及免疫反应

基于生物信息学分析构建预后相关的预测模型,探讨铜死亡相关LncRNA与胃癌（GC）在免疫和预后方面的关系。从癌症基因组图谱（TCGA）数据库下载胃癌患者的RNA测序和临床数据,基于共表达分析筛选出铜死亡相关LncRNA,通过LASSO回归和多因素Cox回归分析构建出与胃癌预后密切相关的铜死亡相关LncRNA风险预测模型,并计算所有胃癌患者样本的风险评分。通过Kaplan-Meier生存分析、回归分析和受试者工作特征（ROC）曲线等证实模型的预后预测性能,并分析风险评分与通路富集分析、免疫浸润细胞、免疫检查点基因、体细胞基因突变及抗癌药物敏感性的相关性。差异表达分析结果表明,LncRNA HAGLR在肿瘤组织中的表达上调。通过实时荧光定量PCR(qRT-PCR)检测LncRNA HAGLR在69例行根治性手术胃癌患者的癌组织及癌旁组织的表达。结果表明,相比于癌旁组织,胃癌组织中HAGLR表达上调,且与肿瘤大小、浸润深度、肿瘤TNM分期、分化程度及淋巴结转移成明显相关性（P<0.05）。本研究构建的铜死亡相关LncRNA预后模型具有较高的预测价值,并且与免疫细胞浸润异质性明显相关,在...     

Construction of a prognostic model for non-small-cell lung cancer based on ferroptosis-related genes

We wished to construct a prognostic model based on ferroptosis-related genes and to simultaneously evaluate the performance of the prognostic model and analyze differences between high-risk and low-risk groups at all levels. The gene-expression profiles and relevant clinical data of patients with non-small-cell lung cancer (NSCLC) were downloaded from public databases. Differentially expressed genes (DEGs) were obtained by analyzing differences between cancer tissues and paracancerous tissues, and common genes between DEGs and ferroptosis-related genes were identified as candidate ferroptosis-related genes. Next, a risk-score model was constructed using univariate Cox analysis and least absolute shrinkage and selection operator (Lasso) analysis. According to the median risk score, samples were divided into high-risk and low-risk groups, and a series of bioinformatics analyses were conducted to verify the predictive ability of the model. Single-sample gene set enrichment analysis (ssGSEA) was used to investigate differences in immune status between high-risk and low-risk groups, and differences in gene mutations between the two groups were investigated. A risk-score model was constructed based on 21 ferroptosis-related genes. A Kaplan–Meier curve and receiver operating characteristic curve showed that the model had good prediction ability. Univariate and multivariate Cox analyses revealed that ferroptosis-related genes associated with the prognosis may be used as independent prognostic factors for the overall survival time of NSCLC patients. The pathways enriched with DEGs in low-risk and high-risk groups were analyzed, and the enriched pathways were correlated significantly with immunosuppressive status.     

Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

There is growing evidence that alternative splicing (AS) plays an important role in cancer development. However, a comprehensive analysis of AS signatures in kidney renal clear cell carcinoma (KIRC) is lacking and urgently needed. It remains unclear whether AS acts as diagnostic biomarkers in predicting the prognosis of KIRC patients. In the work, gene expression and clinical data of KIRC were obtained from The Cancer Genome Atlas (TCGA), and profiles of AS events were downloaded from the SpliceSeq database. The RNA sequence/AS data and clinical information were integrated, and we conducted the Cox regression analysis to screen survival-related AS events and messenger RNAs (mRNAs). Correlation between prognostic AS events and gene expression were analyzed using the Pearson correlation coefficient. Protein-protein interaction analysis was conducted for the prognostic AS-related genes, and a potential regulatory network was built using Cytoscape (version 3.6.1). Meanwhile, functional enrichment analysis was conducted. A prognostic risk score model is then established based on seven hub genes (KRT222, LENG8, APOB, SLC3A1, SCD5, AQP1, and ADRA1A) that have high performance in the risk classification of KIRC patients. A total 46,415 AS events including 10,601 genes in 537 patients with KIRC were identified. In univariate Cox regression analysis, 13,362 survival associated AS events and 8,694 survival-specific mRNAs were detected. Common 3,105 genes were screen by overlapping 13,362 survival associated AS events and 8,694 survival-specific mRNAs. The Pearson correlation analysis suggested that 13 genes were significantly correlated with AS events (Pearson correlation coefficient >0.8 or <−0.8). Then, We conducted multivariate Cox regression analyses to select the potential prognostic AS genes. Seven genes were identified to be significantly related to OS. A prognostic model based on seven genes was constructed. The area under the ROC curve was 0.767. In the current study, a robust prognostic prediction model was constructed for KIRC patients, and the findings revealed that the AS events could act as potential prognostic biomarkers for KIRC.     

Ferroptosis-related gene signature as a prognostic marker for lower-grade gliomas

Ferroptosis is a newly discovered form of programmed cell death, which has unique biological effects on metabolism and redox biology. In this study, the prognostic value of ferroptosis-related genes was investigated in lower-grade gliomas (LGG). We downloaded the ferroptosis-related genes from the FerrDb dataset. Univariate Cox and LASSO regression analyses were applied to identify genes correlated with overall survival (OS). Subsequently, 12 ferroptosis-related genes were screened to establish the prognostic signature using stepwise multivariate Cox regression. According to the median value of risk scores, patients were divided into low- and high-risk subgroups. The Kaplan-Meier curves showed the high-risk group had a lower OS. The predictive power of the risk model was validated using the CGGA. Functional analysis revealed that the terms associated with plasma membrane receptor complex, immune response and glutamate metabolic process were primarily related to the risk model. Moreover, we established a nomogram that had a strong forecasting ability for the 1-, 3- and 5-year OS. In addition, we compared the risk scores between different clinical features. We also detected infiltration of macrophages and monocytes in different subgroups. Overall, our study identified the prognostic signature of 12 ferroptosis-related genes, which has the potential to predict the prognosis of LGG.     

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients’ overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.     

Immune-pyroptosis-related genes predict the prognosis of kidney renal clear cell carcinoma

BackgroundKidney renal clear cell carcinoma (KIRC) is a common cancer of the adult urological system. Recent developments in tumor immunology and pyroptosis biology have provided new directions for kidney cancer treatment. Therefore, there is an urgent need to identify potential targets and prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy.MethodsThe expression of immune-pyroptosis-related differentially expressed genes (IPR-DEGs) between KIRC and healthy tissues was examined using the Gene Expression Omnibus datasets. The GSE168845 dataset was selected for subsequent analyses. Data of 1793 human immune-related genes were downloaded from the ImmPort database (https://www.immport.org./home), while those of 33 pyroptosis-related genes were extracted from previous reviews. The independent prognostic value of IPR-DEGs was determined using differential expression, prognostic, and univariate and multivariate Cox regression analyses. The GSE53757 dataset was used to further verify the GSDMB and PYCARD levels. In our cohorts, the association among DEGs and clinicopathological features and overall survival was analyzed. The least absolute shrinkage and selection operator Cox regression model was established to evaluate the correlation of IPR-DEGs with the immune score, immune checkpoint gene expression, and one-class logistic regression (OCLR) score. KIRC cells and clinical tissue samples were subjected to quantitative real-time polymerase chain reaction to examine the GSDMB and PYCARD mRNA levels. The GSDMB and PYCARD levels in a healthy kidney cell line (HK-2 cells) and two KIRC cell lines (786-O and Caki-1 cells) were verified. The tissue levels of GSDMB and PYCARD were evaluated using immunohistochemical analysis. GSDMB and PYCARD were knocked down in 786-O cells using short-interfering RNA. Cell proliferation was examined using the cell counting kit-8 assay. Cell migration was measured by transwell migration assaysResultsGSDMB and PYCARD were determined to be IPR-DEGs with independent prognostic values. A risk prognostic model based on GSDMB and PYCARD was successfully established. In the GSE53757 dataset, the GSDMB and PYCARD levels in KIRC tissues were significantly higher than those in healthy tissues. The GSDMB and PYCARD expression was related to T stage and OS in our cohort. The GSDMB and PYCARD levels were significantly correlated with the immune score, immune checkpoint gene expression, and OCLR score. The results of experimental studies were consistent with those of bioinformatics analysis. The GSDMB and PYCARD levels in KIRC cells were significantly upregulated when compared with those in healthy kidney cells. Consistently, GSDMB and PYCARD in KIRC tissues were significantly upregulated when compared with those in adjacent healthy kidney tissues. GSDMB and PYCARD knockdown significantly decreased 786-O cell proliferation (p < 0.05). Transwell migration result reflects that silencing GSDMB and PYCARD inhibited 786-O cell migration (p < 0.05) .ConclusionsGSDMB and PYCARD are potential targets and effective prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy in KIRC.     

Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients

Background: Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear.Method: Univariate and least absolute shrinkage and selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application.Results: We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established an FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune checkpoint expression was also significantly positively correlated with the FRSig score. Ultimately, an FRSig-based nomogram model was constructed using the independent prognostic factors age, World Health Organization (WHO) grade, and FRSig score.Conclusion: We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.