Systematic analysis of DNA polymerases as therapeutic targets in pan-cancers

Introduction:DNA polymerases are crucial for maintaining genome stability and influencing tumorigenesis.However,the clinical implications of DNA polymerases in tumorigenesis and their potential as anti-cancer therapy targets are not well understood.Methods:We conducted a systematic analysis using TCGA Pan-Cancer Atlas data and Gene Set Cancer Analysis results to examine the expression profiles of 15 DNA polymerases(POLYs)and their clinical correlations.We also evaluated the prognostic value of POLYs by analyzing their expression levels in relation to overall survival time(OS)using Kaplan-Meier survival curves.Additionally,we investigated the correlations between POLY expression and immune cells,DNA damage repair(DDR)pathways,and ubiquitination.Drug sensitivity analysis was performed to assess the relationship between POLY expression and drug response.Results:Our analysis revealed that 14 out of 15 POLYs exhibited significantly distinct expression patterns between tumor and normal samples across most cancer types,except for DNA nucleotidylexotransferase(DNTT).Specifically,POLD1 and POLE showed elevated expression in almost all cancers,while POLQ exhibited high expression levels in all cancer types.Some POLYs showed heightened expression in specific cancer subtypes,while others exhibited low expression.Kaplan-Meier survival curves demonstrated significant prognostic value of POLYs in multiple cancers,including PAAD,KIRC,and ACC.Cox analysis further validated these findings.Alteration patterns of POLYs varied significantly among different cancer types and were associated with poorer survival outcomes.Significant correlations were observed between the expression of POLY members and immune cells,DDR pathways,and ubiquitination.Drug sensitivity analysis indicated an inverse relationship between POLY expression and drug response.Conclusion:Our comprehensive study highlights the significant role of POLYs in cancer development and identifies them as promising prognostic and immunological biomarkers for various cancer types.Additionally,targeting POLYs therapeutically holds promise for tumor immunotherapy.

Why MUC16 mutations lead to a better prognosis:A study based on The Cancer Genome Atlas gastric cancer cohort

BACKGROUND MUC16,encoding cancer antigen 125,is a frequently mutated gene in gastric cancer.In addition,MUC16 mutations seem to result in a better prognosis in gastric cancer.However,the mechanisms that lead to a better prognosis by MUC16 mutations have not yet been clarified.AIM To delve deeper into the underlying mechanisms that explain why MUC16 mutations signal a better prognosis in gastric cancer.METHODS We used multi-omics data,including mRNA,simple nucleotide variation,copy number variation and methylation data from The Cancer Genome Atlas,to explore the relationship between MUC16 mutations and prognosis.Cox regression and random survival forest algorithms were applied to search for hub genes.Gene set enrichment analysis was used to elucidate the molecular mechanisms.Single-sample gene set enrichment analysis and“EpiDISH”were used to assess immune cells infiltration,and“ESTIMATE”for analysis of the tumor microenvironment.RESULTS Our study found that compared to the wild-type group,the mutation group had a better prognosis.Additional analysis indicated that the MUC16 mutations appear to activate the DNA repair and p53 pathways to act as an anti-tumor agent.We also identified a key gene,NPY1R(neuropeptide Y receptor Y1),which was significantly more highly expressed in the MUC16 mutations group than in the MUC16 wild-type group.The high expression of NPY1R predicted a poorer prognosis,which was also confirmed in a separate Gene Expression Omnibus cohort.Further susceptibility analysis revealed that NPY1R might be a potential drug target for gastric cancer.Furthermore,in the analysis of the tumor microenvironment,we found that immune cells in the mutation group exhibited higher anti-tumor effects.In addition,the tumor mutation burden and cancer stem cells index were also higher in the mutation group than in the wild-type group.CONCLUSION We speculated that the MUC16 mutations might activate the p53 pathway and DNA repair pathway:alternatively,the tumor microenvironment may be involved.

基于The Human Protein Atlas和TCGA数据库分析SmuG1在人卵巢癌中的表达及预后意义

目的通过数据挖掘分析SmuG1在卵巢癌组织中的表达及预后意义。方法 THPA数据库初步探讨SmuG1在人体的分布以及表达, TCGA数据库分析卵巢癌患者SmuG1 mRNA的表达水平与临床病理特征的相关性,通过生存分析探讨SmuG1 mRNA水平与总体生存期的关系,利用Cox比例风险回归模型分析SmuG1是否为患者总体生存期的独立预后影响因子。结果 THPA数据库发现SmuG1是一种细胞内糖基化酶,在全身各类组织及器官中均有表达,TCGA数据库分析发现SmuG1 mRNA水平与卵巢癌的临床分期呈负相关( P =0.024,相关系数 r =-0.5),而与年龄、肿瘤级别、残留肿瘤大小、耐药性等无关( P >0.05)。生存分析发现SmuG1 mRNA水平高的患者比SmuG1 mRNA水平低的患者存活时间更长差异无统计学意义( P =0.035)。Cox比例风险回归模型分析表明SmuG1的表达水平是卵巢癌患者的显著独立预后指标( HR =0.98,95% CI :0.618 97~1.560 583;P =0.009)。结论人卵巢癌组织中SmuG1水平与临床分期呈负相关。SmuG1表达水平高的卵巢癌患者的总体生存期较长,提示SmuG1可能是卵巢癌显著的独立预后指标。

基于TCGA研究左右半结肠癌基因表达差异及苦参-大血藤-半枝莲作用机制

【目的】探讨左半结肠癌与右半结肠癌的基因表达差异及大肠癌核心药对苦参-大血藤-半枝莲作用于左右半结肠癌的机制差异。【方法】下载134例左半结肠癌及194例右半结肠癌患者癌症基因组图谱(TCGA)的转录组数据,应用R软件实现2组差异基因分析及京都基因与基因组百科全书(KEGG)通路富集分析;通过BATMAN-TCM数据库获取苦参-大血藤-半枝莲药对的活性成分及靶点,基于左右半结肠癌差异基因,分别进行药对-左/右半结肠癌KEGG富集分析、蛋白质互作(PPI)网络构建,比较药对治疗左、右半结肠癌富集的生物信号通路差异及关键靶点差异。【结果】左半结肠癌与右半结肠癌相对于正常癌旁组织共同的差异表达基因共6051个,左半结肠癌特异性的差异表达基因共1958个,右半结肠癌特异性的差异表达基因共1739个;左半结肠癌特异性KEGG富集通路14条,右半结肠癌特异性KEGG富集通路23条。苦参-大血藤-半枝莲药对活性化合物共85个,对应的靶点合计469个,药对-左半结肠癌靶点富集于10条KEGG信号通路,关键靶点为DRD2、CACNA1C、HTR3A、COMT、TH,药对-右半结肠癌靶点富集于1条KEGG信号通路,核心靶点为HTR3A、DRD2、TH、AGT、GRIN2B。【结论】左半结肠癌与右半结肠癌存在基因表达差异:左半结肠癌与免疫功能异常、AMPK信号通路异常等机制有关,右半结肠癌与中性粒细胞外陷阱形成、酒精中毒、Hippo信号通路异常等机制有关。除调控细胞周期及必需氨基酸代谢等机制外,苦参-大血藤-半枝莲药对对调节左半结肠癌肠道内分泌功能、抑制右半结肠癌炎症反应具有特异性作用,亦可能对结肠癌患者具有情绪调控作用。

基于TCGA数据库构建肝细胞癌双硫死亡相关基因(DRGs)预后风险模型及评价

目的基于癌症基因组图谱(the cancer genome atlas,TCGA)数据库构建肝细胞癌(hepatocellular carcinoma,HCC)双硫死亡相关基因(disulfidptosis-related genes,DRGs)预后风险模型及评价。方法通过生物信息学方法分析TCGA数据库中371例HCC样本及50例癌旁样本中15个DRGs的表达情况,并进行基因本体(gene ontology,GO)功能注释和京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)富集分析、Kaplan-Meier(KM)生存分析;通过单因素COX回归分析筛选出有统计学意义的DRGs,通过LASSO回归分析及多因素COX回归分析筛选出关键DRGs构建预后风险模型,并根据风险评分将HCC患者分为高风险组和低风险组,制作KM生存曲线和时间依赖的受试者工作特征(receiver operator characteristic,ROC)曲线进行验证评价。结果与癌旁样本相比,HCC样本15个DRGs中FLNA,MYH9,TLN1,ACTB,MYL6,CAPZB,DSTN,ACTN4,SLC7A11,INF2,CD2AP,PDLIM1和FLNB均表达上调,且差异具有统计学意义(t=1793~6310,均P<0.001);经GO功能注释和KEGG富集分析显示,DRGs主要与肌动蛋白细胞骨架和细胞黏附相关的生物过程或途径密切相关。经KM生存分析结果显示,SLC7A11,INF2,CD2AP,MYL6,ACTB高表达组生存率低于低表达组[HR=1.46(1.03~2.07)~1.93(1.36~2.75),均P<0.05]。通过单因素COX回归分析、LASSO分析及多因素COX回归分析构建预后风险模型riskscore=(0.247×SLC7A11)+(0.289×INF2)+(0.076×CD2AP)+(0.06×MYL6);计算样本的风险评分,风险评分越高,预后不良的HCC患者人数越多;KM生存分析显示高风险组的总生存率比低风险组低;1,3,5年的AUC值分别为0.709,0.661和0.648;通过多因素COX回归分析表明SLC7A11[HR=1.832(1.274~2.636),P=0.001]是独立的预后危险因素。结论四个DRGs构建的预后风险模型在预测HCC患者预后情况有一定的作用。

利用TCGA和GEPIA2公共数据库分析ACSM3与卵巢癌预后的关系

目的 研究肿瘤基因图谱(TCGA)数据库中酰基辅酶A合成酶中链家族成员3(ACSM3)在357例卵巢癌患者中的表达量及其与患者生存状况的关系。方法 对TCGA数据库中357例卵巢癌患者的ACSM3表达量及其与生存情况的关系进行分析,寻找其共表达基因并进行基因本体(GO)富集分析和京都基因与基因组百科全书(KEGG)富集分析。结果 多因素Cox回归分析进一步表明,ACSM3是卵巢癌患者预后的影响因素,其共表达基因富集分析结果与炎症应答和肿瘤中基因异常转录相关。结论 ACSM3的表达对卵巢癌患者的预后具有重要的意义,高表达预后较好。ACSM3可以成为指导临床医师评估卵巢癌患者预后的重要指标。

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer

BACKGROUND Breast cancer(BC),a leading malignant disease,affects women all over the world.Cancer associated fibroblasts(CAFs)stimulate epithelial-mesenchymal transition,and induce chemoresistance and immunosuppression.AIM To establish a CAFs-associated prognostic signature to improve BC patient out-come estimation.METHODS We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas(TCGA)databases,and 3661 BC samples from the The Gene Expression Omnibus.CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm.CAF-associated gene identification was done by weighted gene co-expression network analysis.A CAF risk signature was established via univariate,least absolute shrinkage and selection operator regression,and mul-tivariate Cox regression analyses.The receiver operating characteristic(ROC)and Kaplan-Meier curves were employed to evaluate the predictability of the model.Subsequently,a nomogram was developed with the risk score and patient clinical signature.Using Spearman's correlations analysis,the relationship between CAF risk score and gene set enrichment scores were examined.Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction(qRT-PCR).RESULTS Employing an 8-gene(IL18,MYD88,GLIPR1,TNN,BHLHE41,DNAJB5,FKBP14,and XG)signature,we attemp-ted to estimate BC patient prognosis.Based on our analysis,high-risk patients exhibited worse outcomes than low-risk patients.Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis.ROC analysis exhibited satisfactory nomogram predictability.The area under the curve showed 0.805 at 3 years,and 0.801 at 5 years in the TCGA cohort.We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes.CAF risk score was significantly correlated with most hallmark gene sets.Finally,the prognostic signature were further validated by qRT-PCR.CONCLUSION We introduced a newly-discovered CAFs-associated gene signature,which can be employed to estimate BC patient outcomes conveniently and accurately.

基于TCGA数据库分析乳腺癌组织RBP7 mRNA表达与肿瘤免疫细胞浸润及预后的相关性

目的通过生物信息学的方法探讨视黄醇结合蛋白7(retinol binding protein 7,RBP7)在乳腺癌中的作用。方法使用R语言基于癌症基因组图谱(the cancer genome atlas,TCGA)数据库和人类蛋白质图谱(the human protein atlas,HPA)数据库探索基因RBP7在乳腺癌组织中的差异表达。通过Kaplan-Meier生存分析和受试者工作特征(receiver operating characteristic,ROC)曲线,评估RBP7与乳腺癌临床数据的关系。基于TCGA数据库分析RBP7高低表达分组与不同肿瘤浸润免疫细胞(tumor-infiltrating immune cells,TIICs)的相关性。基因组富集分析(gene set enrichment analysis,GSEA)评估RBP7在与表型相关度排序的基因表中的分布趋势。结果与癌旁组织相比,乳腺癌中RBP7 mRNA表达水平下调,该分子表达在细胞核中。ROC曲线分析显示RBP7诊断乳腺癌的曲线下面积(area under curve,AUC)是0.943(95%CI:0.926~0.960),RBP7的最佳截断值是6.29,敏感度和特异度分别为82.32%,93.69%。Kaplan-Meier生存分析显示RBP7低表达与乳腺癌患者的总生存率相关(HR=0.68,95%CI:0.49~0.93,P=0.017),RBP7是乳腺癌发生的独立危险因素。Spearman相关性揭示RBP7与乳腺癌中pDC细胞和NK细胞呈正相关(r=0.290,0.253,均P<0.05),与Th2细胞呈负相关(r=-0.217,P<0.05)。GSEA表明RBP7富集在脂肪生成、核糖体、肽配体结合受体、钙信号途径等通路中(均P<0.001)。结论RBP7影响乳腺癌的发生发展,可能成为乳腺癌潜在生物标志物和治疗靶点。

基于TCGA数据库角蛋白家族基因对肺腺癌病人预后的预测价值

目的:探讨角蛋白(KRT)家族基因对肺腺癌(LUAD)病人预后的预测价值。方法:RNA测序数据来自LUAD肿瘤和配对正常组织的癌症基因组图谱(TCGA)数据库。采用多变量COX比例风险回归分析评估KRT家族成员基因的预后价值。分析筛选变量以构建风险评分,时间相关ROC曲线评估预测结果。采用使用Nomogram评估个体化预后风险。结果:从差异表达基因中筛选出14个在LUAD肿瘤及癌旁组织中存在显著失衡的KRT基因。ROC曲线分析证实,该14个KRT基因可以作为LUAD诊断的潜在诊断标志物。6个KRT基因与肺癌预后相关,结果显示KRT8和KRT6A是影响LUAD预后的独立危险因素。结论:KRT8和KRT6A可作为LUAD的预后指标,KRT8和KRT6A高表达表明LUAD病人的预后较差。

基于TCGA数据库探究SERPINA5在前列腺癌中的表达及临床意义

目的:研究前列腺癌中关键基因的差异化表达,为前列腺癌的诊疗提供新的生物治疗靶点和理论依据。方法:从美国癌症基因组图谱(TCGA)中下载前列腺癌的相关基因数据,利用R软件筛选出在前列腺癌组织和癌旁组织中差异化表达的基因,并进行富集分析。利用STRING 11.0构建差异基因所表达蛋白的互作网络图,并通过Cytoscape_v3.7.1进一步筛选Hub基因,通过Oncomine数据库对Hub基因在前列腺癌中的表达进行验证得到关键基因,随后利用R软件对关键基因与临床数据进行统计分析,最终利用在线工具GEPIA对关键基因进行生存分析。结果:总共得到551个样本(其中癌旁组织样本52个,癌组织样本499个),筛选后得到463个差异基因,其中呈现上调趋势的基因共265个,下调198个。富集分析后发现这些基因主要富集在顶体反应的调节、三酰甘油代谢过程,PPAR信号通路等,进一步筛选后得到10个Hub基因,与Oncomine数据库联合验证后得到关键基因SERPINA5,进行临床分析发现前列腺癌病人的SERPINA5的表达水平与肿瘤分化程度、TNM分期以及无病生存率有关(P<0.05),而与年龄、人种以及总生存率的差异无关(P>0.05)。结论:前列腺癌中存在大量差异基因可能对疾病的发生发展起到影响作用,其中SERPINA5也许可以作为前列腺癌早期筛查标志物或治疗的生物靶点,为前列腺癌的预防与治疗带来新的方案以及研究方向。

基于TCGA数据分析NEK2基因在恶性胸膜间皮瘤中的表达及预后意义

目的 分析永不有丝分裂基因A相关激酶2(NEK2)基因在恶性胸膜间皮瘤(MPM)中的表达及预后意义。方法 下载癌症基因组图谱(TCGA)数据库MPM基因表达数据、临床病理数据和生存状态数据;使用perl软件和R语言的limma、timeROC等程序包作NEK2基因在MPM中的差异分析、临床病理相关性分析、COX单因素和多因素分析以及基因富集分析,并绘制ROC曲线;利用TIMER2.0数据库分析MPM中NEK2表达与常见的免疫细胞浸润的相关性。选取12例MPM组织和6例非MPM胸膜组织,通过RT-qPCR的方法验证NEK2基因在MPM与非瘤组织中的表达情况。结果 与正常肺组织相比,NEK2在MPM组织中高表达(P<0.001)。NEK2基因mRNA表达量与MPM淋巴结分期具有显著相关性(P<0.05);NEK2高表达与MPM患者预后不良相关(P<0.001);NEK2基因表达与MPM中B淋巴细胞、巨噬细胞、树突状细胞浸润水平呈正相关(P<0.05),并在细胞周期、DNA复制、剪接体、mRNA监测等通路显著富集。在收集到的病例样本中,与非MPM胸膜组织相比,NEK2基因在MPM组织中的表达量显著增高(P<0.01)。结论 NEK2在MPM组织中高表达,且NEK2在MPM中高表达提示预后不良,可能成为治疗MPM的潜在靶点。

基于TCGA数据分析TRIM45基因在乳腺癌中的表达特征及生物学功能

目的基于GDC TCGA Breast Cancer(BRCA)的数据,评估TRIM45(Tripartite Motif Family 45)在乳腺癌中的表达、预后价值及其与临床病理的相关性,揭示TRIM45在乳腺癌中的生物学功能及可能的作用机制。方法采用R软件对GDC TCGA Breast Cancer(BRCA)数据进行生信分析,研究TRIM45在乳腺癌中的表达及其与临床病理的关系。采用Kaplan-Meier法评估TRIM45对乳腺癌预后的影响。应用KEGG基因集进行GSEA(Gene Set Enrichment Analysis)基因富集分析,采用Spearman相关性分析筛选TRIM45相关作用基因。结果TRIM45在乳腺癌临床样本中的表达高于癌旁组织(P<0.001);Kaplan-Meier生存分析显示TRIM45高表达患者的总生存期高于TRIM45低表达者(P<0.05);TRIM45在乳腺癌中的表达与T分期、TNM分期、分子分型及病理类型相关联(P<0.05);GSEA富集分析结果显示TRIM45在KEGG_PATHWAYS_IN_CANCER信号通路中发挥重要的生物学作用,Spearman相关性分析筛选出TRIM45与STK36、IKBKB、GLI3具有正相关关系(P<0.001),与HIF1A具有负相关关系(P<0.001)。结论TRIM45在乳腺癌中的表达与T分期、TNM分期、分子分型及病理类型相关联;TRIM45可能通过调控STK36、IKBKB、GLI3和HIF1A在乳腺癌发生发展中发挥重要作用。

基于TCGA数据库分析SSBP1基因在肝细胞癌中的表达及预后

目的利用癌症基因组图谱(TCGA)数据库研究SSBP1基因在肝细胞癌(HCC)组织中的表达情况,并探讨SSBP1基因表达量与患者临床病理特征及预后的相关性。方法基于TCGA数据库提取374例HCC组织及50例正常肝组织中SSBP1基因表达量数据、患者的临床病理参数资料,利用R 4.1.2分析SSBP1在HCC中的表达情况及其与HCC患者的临床病理特征和预后的相关性,通过基于基因表达水平值的交互式分析平台(GEPIA)数据库和R 4.1.2联合分析SSBP1在HCC各临床病理分期间的表达差异,利用TIMER数据库分析SSBP1表达与免疫细胞浸润的相关性,通过R软件pROC程序包制作ROC生存曲线评估SSBP1表达量对HCC的诊断效果。结果TCGA数据库分析表明,无论是配对样本还是非配对样本,SSBP1 mRNA在HCC组织中的表达水平均高于正常肝组织(P均<0.001),且表达水平与性别和M分期有关(P均<0.05),与年龄、T分期、AJCC分期等无关(P均>0.05)。进一步进行单因素和多因素Cox回归分析发现,SSBP1高表达是影响HCC患者总生存期(OS)的独立危险因素(HR=1.713,P=0.016)。免疫细胞浸润分析发现,HCC组织中SSBP1表达与幼稚B细胞浸润水平呈负相关,而与记忆B细胞和巨噬细胞呈正相关(|r|>0.2,P均<0.05)。ROC曲线分析显示,SSBP1 mRNA表达水平对HCC的早期诊断和预后均具有良好的评估价值(AUC>0.50)。结论SSBP1 mRNA在HCC中的表达增高,且高表达是预后不良的独立危险因素,同时SSBP1对HCC具有良好的诊断价值,可成为HCC患者预后判断一种潜在的分子标志物以及免疫治疗的潜在靶点。

基于TCGA数据库构建子宫内膜癌预后模型

目的采用生物信息学方法构建子宫内膜癌(EC)患者预后的模型。方法从TCGA数据库下载与EC相关预后数据,将从TCGA中下载的数据分为训练组(Train组)和验证组(Test组)。使用Train组中的数据建立预后模型,并在Test组中验证此预后模型是否具有良好的预测能力。结果采用单因素Cox回归筛选出229个与生存非常相关的基因,筛选出有特异性的用于建立风险模型的基因为16个。利用随机生存森林算法(random survival forests algorithm)二次筛选出9个与生存较相关的基因用来组成风险预后模型,其中MGAT4A、SRD5A1、MBOAT2、EPHA10、L1CAM、CHODL、AC010729.1、RP11-66D17、RP5-1158E12.3均被认为是HR>1的风险效应因子(P<0.05),风险评分计算公式为:risk score=∑iwixi+1.802659。risk score的评判公式对这些样本进行了高风险和低风险的评估,在Train组、Test组绘制K-M生存曲线,结果显示高风险组总生存率均低于低风险组(P<0.05)。结论(1)通过风险模型计算的评分可以将子宫内膜癌患者分成高危组及低危组,两组生存状态比较差异有统计学意义(P<0.001);(2)此预后相关风险模型可以有效预测EC患者预后,为EC临床诊疗提供新依据。

基于TCGA数据库肺腺癌自噬相关基因预后模型的建立与验证

目的探究肺腺癌自噬相关基因(ARGs),并构建肺腺癌ARGs的预后模型。方法肺腺癌的RNA高通量转录组数据下载于癌症基因组图谱(TCGA)数据库和人类自噬基因(HADb)数据库,获取ARGs,并基于肺腺癌差异表达ARGs构建肺腺癌预后模型并验证,进一步构建列线图及校准曲线探究模型在临床中的应用价值。对差异表达ARGs进行基因本体和京都基因与基因组百科全书富集分析。对获得的具有预后意义的差异表达ARGs进行Lasso回归分析并构建肺腺癌预后模型。绘制Kaplan-Meier生存曲线。结果共获得31个差异表达ARGs,筛选得到10个具有预后意义的差异表达ARGs。高风险组患者与较差的总生存期明显相关,差异有统计学意义(P<0.05)。T分期、N分期、风险评分为肺腺癌患者的独立预后因素。校准曲线和列线图全局一致性为0.710,说明模型预测结果与实际情况具有较高的符合度。结论基于差异表达ARGs构建的风险模型可作为肺腺癌患者的一种预后特征或可为肺腺癌患者的个体化治疗提供参考依据。

基于TCGA探讨急性髓系白血病患者miR-363表达与其临床特征及靶基因的关系

目的 探讨急性髓系白血病(AML)患者miR-363表达与其临床特征及靶基因的关系。方法 下载TCGA数据库中AML患者的miRNA、RNA表达谱及临床数据,按照miR-363表达水平由高到低排序,将前50%设为高表达组(n=81),后50%设为低表达组(n=81),比较两组患者生存曲线及年龄差异;利用Omisc软件,采用Pearson相关性分析筛选与miR-363表达相关的靶基因。将2021年12月至2022年12月在我院诊治的52例AML患者纳入研究,采用RT-qPCR技术检测患者骨髓miR-363表达水平,按照表达水平由高到低排序,将前50%设为高表达组(n=26),后50%设为低表达组(n=26),比较两组患者初诊白细胞计数、血小板计数、血红蛋白及血浆靶基因水平、年龄、性别及预后分层的差异。结果 TCGA数据库分析显示,miR-363低表达组患者中位生存时间高于miR-363高表达组(822d vs 365d,P<0.05);miR-363高表达组高龄患者比例明显高于低表达组(54.32%vs 45.68%,P<0.05);共筛选出5 404个miR-363相关基因,KIAA1377相关性最高(r=0.62,P<0.05)。miR-363高表达组高龄患者比例、预后不良患者比例、初诊白细胞计数≥50×109/L比例、血浆KIAA1377水平明显高于低表达组(均P<0.05);血小板计数、血红蛋白水平明显低于低表达组(均P<0.05)。结论 miR-363高表达与AML患者生存率降低、高龄、预后不良、初诊白细胞计数升高、血小板计数及血红蛋白水平降低有关,且KIAA1377可能是miR-363的靶基因之一。miR-363可能是AML基因治疗和预后评估的新靶点。

基于TCGA数据库研究KCNT2在肺腺癌中的表达及与患者预后的相关性

目的基于癌症基因组图谱(TCGA)数据库,观察钠激活钾通道蛋白基因KCNT(potassium sodium-activated channel subfamily T member 2,KCNT2)在肺腺癌癌组织中的表达及临床意义。方法从TCGA数据库中下载肺腺癌组织的RNASeq数据以及临床数据,分析KCNT2 mRNA在肺腺癌组织和正常组织中的表达差异;以KCNT2表达水平的中位值(0.143)为界限将肺腺癌患者分为KCNT2高表达组和KCNT2低表达组,应用单因素及多因素COX回归分析癌组织中KCNT2表达与患者临床病理特征的关系及预后的相关性。应用基因集富集分析(GSEA)软件分析预测KCNT2在肺腺癌中可能的调控信号通路。结果肺腺癌组KCNT2表达水平低于正常对照组(P<0.05);肺腺癌组织中KCNT2的表达水平与患者Stage分期相关(P<0.05);Stage分期(HR=1.947,95%CI:1.236~3.066,P=0.004)可以作为肺腺癌的独立预后因素;KCNT2低表达组生存率低于KCNT2高表达组(P=0.035);KCNT2主要参与氧化磷酸化信号通路、核糖体、嘧啶代谢、精氨酸和脯氨酸代谢、谷胱甘肽代谢、RNA聚合酶等信号通路。结论KCNT2在肺腺癌组织中呈低表达,与肺腺癌患者的预后相关,并通过参与多种信号通路促进肺腺癌的发生发展,可能是肺腺癌诊断和治疗的潜在生物标志物。

Targeted therapies in metastatic gastric cancer: Current knowledge and future perspectives

Gastric cancer(GC)represents a leading cause of cancer related morbidity and mortality worldwide accounting for more than 1 million of newly diagnosed cases and thousands of deaths every year.In the last decade,the development of targeted therapies and the optimization of already available chemotherapeutic drugs has expanded the available treatment options for advanced GC and granted better survival expectations to the patients.At the same time,global efforts have been undertaken to investigate in detail the genomic and epigenomic heterogeneity of this disease,resulting in the identification of new specific and sensitive predictive and prognostic biomarkers and in innovative molecular classifications based on gene expression profiling.Nonetheless,several randomized studies aimed at exploring new innovative agents,such as immune checkpoint inhibitors,failed to demonstrate clinically meaningful survival advantages.Therefore,it is essential to further improve the molecular characterization of GC subgroups in order to provide researchers and medical oncologists with new tools for patients’selection and stratification in future clinical development programs and subsequent trials.The aim of the present manuscript is to provide a global overview of the recent molecular classifications from The Cancer Genome Atlas and the Asian Cancer Research Group and to present key promising developments in the field of immunotherapy and targeted therapies in metastatic GC.

Emerging molecular classifications and therapeutic implications for gastric cancer

Gastric cancer(GC) is a highly aggressive and life-threatening malignancy.Even with radical surgical removal and front-line chemotherapy,more than half of GCs locally relapse and metastasize at a distant site.The dismal outcomes reflect the ineffectiveness of a one-size fits-all approach for a highly heterogeneous disease with diverse etiological causes and complex molecular underpinnings.The recent comprehensive genomic and molecular profiling has led to our deepened understanding of GC.The emerging molecular classification schemes based on the genetic,epigenetic,and molecular signatures are providing great promise for the development of more effective therapeutic strategies in a more personalized and precise manner.To this end,the Cancer Genome Atlas(TCGA) research network conducted a comprehensive molecular evaluation of primary GCs and proposed a new molecular classification dividing GCs into four subtypes:Epstein-Barr virus-associated tumors,microsatellite unstable tumors,genomically stable tumors,and tumors with chromosomal instability.This review primarily focuses on the TCGA molecular classification of GCs and discusses the implications on novel targeted therapy strategies.We believe that these fundamental findings will support the future application of targeted therapies and will guide our efforts to develop more efficacious drugs to treat human GCs.

基于TCGA数据库构建三阴性乳腺癌预后相关的ceRNA调控网络及分析

目的通过分析癌症基因组图谱(the cancer genome atlas,TCGA)数据库构建三阴性乳腺癌(triple negative breast cancer,TNBC)预后相关的竞争性内源性核糖核酸(competitive endogenous RNA,ceRNA)调控网络。方法从TCGA数据库中下载TNBC lncRNA表达RNAseq数据,对TNBC患者的mRNA,miRNA和lncRNA进行差异表达分析,并进一步行生存分析,得到与乳腺癌有明显差异表达同时也对生存有相关性的mRNA,miRNA和lncRNA。同时构建lncRNA-miRNA-mRNA相关ceRNA调控网,再对生存相关lncRNA所相关的mRNA进一步功能富集和注释,并构建蛋白质互作网络最终用关键基因通过人类蛋白质图谱(the human protein atlas,HPA)数据库进行验证。结果在TCGA中共找到TNBC差异表达mRNA 2331个、差异miRNA 100个和差异lncRNA 1269个。ceRNA调控网中的mRNA在细胞黏附、唾液分泌和血小板活化、用于IgA产生的肠道免疫网络、补体和凝血级联反应等信号通路中明显富集。生存分析中,1个差异mRNA(NMUR1),1个差异表达miRNA(hsa-miR-6832-3p),2个差异表达的lncRNA(AC104809,LINC01297)的表达量均与TNBC患者的预后相关,差异具有统计学意义(P<0.05)。最后利用HPA数据库对NMUR1蛋白水平和生存分析验证,NMUR1的高表达患者的总生存期显著高于NMUR1低表达组,差异有统计学意义(P<0.05)。结论成功构建了促进TNBC发生发展的lncRNA-miRNA-mRNA调控网络,筛选得到生存相关的差异mRNA,miRNA和lncRNA为TNBC发病机制的研究和诊疗生物标志物的探索提供参考依据。