肺腺癌关键基因的鉴别及预后

目的 利用生物信息学方法对肺腺癌基因表达谱进行研究,筛选出该病发生相关的关键基因.方法 通过对GEO(Gene Expression Omnibus)数据库中肺腺癌患者基因芯片数据的检索获取基因表达数据,利用美国国立卫生研究院提供的GEO2R基因差异表达在线分析工具进行数据分析,然后对差异基因进行GO和KEGG富集分析...     

肺腺癌预后相关的炎症反应关键基因筛选及其预后预测模型建立

目的 筛选基于肺腺癌（LUAD）预后相关的炎症反应关键基因,并基于该基因构建预后预测模型。方法 在TCGA数据库中下载肺腺癌组织数据作为训练集,在GTEx数据库中下载正常肺组织数据作为训练集的正常对照,筛选差异表达基因（DEG）;在分子特征数据库中下载炎症反应相关基因列表,采用单变量COX回归分析其中与预后相关的炎症反应相关基因,与DEG取交集得到与LUAD预后相关的炎症反应相关基因,应用LASSO回归和随机生存森林（RSF）算法筛选与LUAD预后相关的炎症反应关键基因,并建立预后风险评分公式。使用训练集进行内部验证,从GEO数据库中下载LUAD数据作为验证集进行外部验证,绘制该预后风险评分预测患者1年、3年和5年生存率的受试者工作特征（ROC）曲线,根据cut-off值分为高、低风险组,比较其总生存期（OS）。单因素及多因素COX回归分析风险评分与训练集和验证集OS的关系,整合所有独立的预后相关因素,构建预测训练集患者1年、3年和5年生存率的列线图。结果 LUAD组织和正常肺组织的DEG共48个,与预后相关的炎症反应相关基因共50个,取交集后获得与LUAD预后相关的炎症反应相关基因共...     

抑癌基因P16在肺鳞癌和肺腺癌中的表达及临床意义

目的 探讨p16基因产物在肺鳞癌和肺腺癌中的表达及其意义。方法 本组56例原发性非小细胞肺癌，其中鳞癌37例，腺癌19例。用免疫组织化学PCR法检测患者肺癌新鲜标本p16蛋白表达水平。结果 56例肺癌标本中p16蛋白阳性表达率为58.9%（33／56），伴有淋巴结转移者其阳性表达率41.4%（12／29）显著低于无淋巴结转移者（P16阳性表达率为77.8%（21／27），P＜0.01）。P16蛋白阴性表达者的1年、3年生存率分别为59.7%、44.1%，显著低于p16蛋白阳性表达者85.2%、71.8%。结论 p16蛋白表达与肺鳞癌和肺腺癌的组织类型，淋巴结转移及预后有关。p16蛋白状态可作为判断肺癌预后的指标之一。     

肺腺癌预后关键基因的筛选、验证及其调控通路分析

目的筛选肺腺癌预后关键基因并进行验证,分析其调控通路。方法从TCGA和GEO数据库获取肺腺癌转录组数据,筛选共同差异表达基因。将LASSO引入到COX回归模型中,进一步筛选预后关键基因。计算TCGA数据库获得的500例肺腺癌患者的预后关键基因相关风险评分,以风险评分中位数作为临界值将患者分为高风险组和低风险组,比较两组5年生存率。采用GEPIA数据库分析癌组织预后关键基因表达,Kaplan Meier-plotter数据库分析预后关键基因表达与肺腺癌患者预后的关系。采用基因集变异分析(GSVA)预测肺腺癌预后关键基因的调控通路。结果在TCGA、GEO数据库共得到166个共同差异表达基因,回归分析筛选出DCN、RRAS、ECT2和PCP4是肺腺癌预后关键基因。高、低风险组5年生存率分别为29.3%、48.4%,两组比较P <0.01。肺腺癌组织中DCN、RRAS mRNA表达均低于正常肺组织,PCP4、ECT2 mRNA表达均高于正常肺组织(P均<0.05)。RRAS、PCP4、ECT2高表达者5年生存率明显低于低表达者,DCN高表达者5年生存率明显高于低表达患者(P均<...     

加权基因共表达网络分析鉴别与老年肺腺癌免疫相关的关键基因筛选

目的:筛选与老年肺腺癌患者肿瘤浸润免疫细胞相关的关键基因。方法:回顾性分析,训练集的基因表达数据来源于癌症基因组图谱数据库,验证集来源于基因表达综合数据库的GSE72094数据集,筛选年龄≥75岁的肺腺癌患者91例,14例匹配的正常样本。肿瘤浸润免疫细胞水平由反卷积算法计算,训练集采用加权基因共表达网络分析筛选与肿瘤浸...     

K—ras基因与肺腺癌

Ｋ－ｒａｓ基因突变主要发生在肺腺癌，吸烟是促发Ｋ－ｒａｓ基因突变的重要因素。Ｋ－ｒａｓ基因突变是肺腺癌估计复发、判断预后的良好指标。在肿瘤发生时，细胞中原癌基因被激活成为癌基因，从而使细胞正常生长、增殖、分化及调控紊乱，继而发展成肿瘤细胞。近年来Ｋｉｒｓｔｅｎ－ｒａｓ（Ｋ－ｒａｓ）基因与肺癌，特别是与肺腺癌的关系日益受关注。本拟对近年来Ｋ－ｒａｓ基因点突变与肺腺癌的关系予以综述。     

应用基因表达谱芯片筛选胃腺癌相关基因

目的:利用基因芯片技术筛选胃腺癌组织和癌旁正常组织间的差异表达基因．方法:分别抽取胃腺癌组织和癌旁正常组织的总RNA．采用逆转录的方法,制成cDNA链, 并以两种荧光Cy5和Cy3标记后作为探针,与含有14 784条人类14KcDNA基因表达谱芯片进行杂交．以Agilent荧光扫描仪扫描芯片上两种荧光信号,并用计算机处理和分析．结果:在14 784条基因中,4例胃腺癌组织和癌旁正常组织共同差异表达基因29条,其中上调基因10条,下调基因19条,上调的基因中有2 条功能信息不明．结论:胃腺癌发生过程中有多基因的参与,胃腺癌与癌旁正常组织共同差异表达的29条基因可能与胃癌的发生、发展有关．     

TP53基因突变肺腺癌患者的临床特征及预后

目的探讨TP53基因突变肺腺癌患者的临床特征及预后。方法回顾性分析2017年10月至2018年12月间57例肺腺癌患者,分析TP53基因突变与未突变组的临床特征,比较两组的无疾病进展生存期(PFS)。结果 57例肺腺癌患者中,TP53突变患者为26例,突变率为45. 6%。TP53基因突变多见于吸烟以及ⅢB-Ⅳ期患者,差异具有统计学意义(P 0. 05)。在性别、年龄、合并EGFR突变以及肿瘤分化程度方面两组之间无统计学差异。两组的中位PFS分别为3. 05个月和6. 00个月,差异具有统计学意义(P 0. 01)。结论 TP53基因突变多见于吸烟以及晚期肺腺癌患者,TP53基因突变提示着不良的预后。     

GASC1在肺腺癌组织中表达的临床意义及预后价值

目的 探讨GASC1在肺腺癌组织中表达的临床意义及预后价值.方法 采用Q-PCR、免疫组织化学、Western blot技术检测100例肺腺癌组织中GASC1蛋白的表达,并分析GASC1蛋白表达与肺腺癌临床病理特征及预后的相关性.结果 GASC1高表达与肺腺癌分化程度、淋巴结转移、临床分期显著相关(P<0.05).GA...     

手术切除肺腺癌患者免疫细胞浸润分析及预后关系

目的探究肺腺癌手术患者免疫细胞浸润模式及预后关系。方法基于GSE68465数据集,利用CIBERSORT软件包,对442例样本中22种免疫细胞进行定量分析,利用Survival包,Kaplan-Meier法分析22种免疫细胞含量与总生存率关系,利用Cox多变量回归分析构建肺腺癌手术患者免疫细胞预后风险模型,根据风险评分中位数,分为高风险组和低风险组,绘制Kaplan-Meier生存曲线和ROC曲线,评估模型的预测效果。结果肺腺癌组织浸润的免疫细胞主要有浆细胞、M2巨噬细胞和M0巨噬细胞,肺腺癌组织与正常组织免疫浸润存在显著差异。静息NK细胞与预后关系显著(P<0.05)。基于5种免疫细胞构建预后风险模型(Risk Score=8.156×静息NK细胞+9.059×活化CD4+记忆T细胞+3.899×活化肥大细胞+2.452×M0巨噬细胞+5.575×活化树突状细胞),高风险组较低风险组预后显著较差(P<0.0001),ROC曲线提示该风险模型具有较好的预后预测效果。结论免疫细胞浸润风险评分模型可以有效预测肺腺癌手术患者预后。     

Identification of hub genes correlated with sleep deprivation using co-expression analysis

Background

Sleep deprivation (SD) has become a serious concern worldwide. This study aimed to identify key modules and candidate hub genes correlated with diseases caused by SD, using co-expression analysis.

Methods

The weighted gene co-expression network analysis was performed to construct a co-expression network of hub genes correlated with SD. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to search for signaling pathways. The protein–protein interaction network analysis of central genes was performed to recognize the interactions among central genes. Molecular Complex Detection, a plugin in Cytoscape, was used to discover the hub gene clusters involved in SD.

Results

A total of 564 genes in the yellow module were identified based on the results of topological overlap measure–based clustering. The yellow module showed a pivotal correlation with SD. Six hub gene clusters prominently associated with SD were identified. Heat shock protein family and circadian clock genes among them may be the hub genes involved in SD.

Conclusions

These genes and pathways might become therapeutic targets with clinical usefulness in the future.

Graphical abstract

     

Identification of prognostic tumor-infiltrating immune cells in endometrial adenocarcinoma

To identify prognostic tumor-infiltrating immune cells of endometrial adenocarcinoma.The gene expression profiles of endometrial adenocarcinoma were downloaded from the Cancer Genome Atlas (TCGA). The abundance of tumor-infiltrating immune cells in endometrial adenocarcinoma samples was calculated by CIBERSORT algorithm. Kaplan–Meier analysis was used to identify prognostic tumor-infiltrating immune cells.This study identified 22 kinds of tumor-infiltrating immune cells. Macrophages M0 and CD8 T cells were prognostic factors of endometrial adenocarcinoma. The abundance of macrophages M0 (P = .038) was significantly correlated with better prognosis of endometrial adenocarcinoma. In contrast, the abundance of CD8 T cells (P = .049) was associated with poor prognosis of endometrial adenocarcinoma.Tumor-infiltrati macrophages M0 and CD8 T cells were prognostic factors of endometrial adenocarcinoma.     

Identification of hub genes and therapeutic drugs in rheumatoid arthritis patients

Clinical Rheumatology - Rheumatoid arthritis (RA) is considered a chronic autoimmune inflammatory disease that causes great morbidity and shortens life expectancy; however, the precise pathogenesis...     

A promising prognostic signature for lung adenocarcinoma (LUAD) patients basing on 6 hypoxia-related genes

Background:Hypoxia signaling plays a critical role in the development of lung adenocarcinoma (LUAD). We herein aimed to explore the prognostic value of hypoxia-related genes and construct the hypoxia-related prognostic signature for LUAD patients.Methods:A total of 26 hypoxia-related genes were collected. Five hundred thirteen and 246 LUAD samples were obtained from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Univariate Cox regression and LASSO Cox regression analyses were conducted to screen the hypoxia-related genes associated with the prognosis of LUAD patients, which would be used for constructing prognosis predictive model for LUAD patients. Multivariate Cox regression analysis was done to determine the independent prognostic factors. The Nomogram model was constructed to predict the prognosis of LUAD patients.Results:Based on 26 hypoxia-related genes, LUAD samples could be divided into 4 clusters with different prognoses. Among which, 6 genes were included to construct the Risk Score and the LUAD patients with higher Risk Score had worse prognosis. Besides, the Nomogram based on all the independent risk factors could relatively reliably predict the survival probability. And 9 types of immune cells’ infiltration was significantly differential between high and low risk LUAD patients.Conclusion:The Risk Score model based on the 6 crucial hypoxia-related genes could relatively reliably predict the prognosis of LUAD patients.