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.

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.

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.

Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus

BACKGROUND Breast cancer is regarded as a highly malignant neoplasm in the female population,posing a significant risk to women’s overall well-being.The prevalence of breast cancer has been observed to rise in China,accompanied by an earlier age of onset when compared to Western countries.Breast cancer continues to be a prominent contributor to cancer-related mortality and morbidity among women,primarily due to its limited responsiveness to conventional treatment modalities.The diagnostic process is challenging due to the presence of non-specific clinical manifestations and the suboptimal precision of conventional diagnostic tests.There is a prevailing uncertainty regarding the most effective screening method and target populations,as well as the specificities and execution of screening programs.AIM To identify diagnostic and prognostic biomarkers for breast cancer.METHODS Overlapping differentially expressed genes were screened based on Gene Expression Omnibus(GSE36765,GSE10810,and GSE20086)and The Cancer Genome Atlas datasets.A protein-protein interaction network was applied to excavate the hub genes among these differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses,as well as gene set enrichment analyses,were conducted to examine the functions of these genes and their potential mechanisms in the development of breast cancer.For clarification of the diagnostic and prognostic roles of these genes,Kaplan–Mei-er and Cox proportional hazards analyses were conducted.RESULTS This study demonstrated that calreticulin,heat shock protein family B member 1,insulin-like growth Factor 1,interleukin-1 receptor 1,Krüppel-like factor 4,suppressor of cytokine signaling 3,and triosephosphate isomerase 1 are potential diagnostic biomarkers of breast cancer as well as potential treatment targets with clinical implications.CONCLUSION The screening of biomarkers is of guiding significance for the diagnosis and prognosis of the diseases.

Comprehensive analysis of distal-less homeobox family gene expression in colon cancer

BACKGROUND The distal-less homeobox(DLX)gene family plays an important role in the development of several tumors.However,the expression pattern,prognostic and diagnostic value,possible regulatory mechanisms,and the relationship between DLX family genes and immune infiltration in colon cancer have not been systematically reported.AIM We aimed to comprehensively analyze the biological role of the DLX gene family in the pathogenesis of colon cancer.METHODS Colon cancer tissue and normal colon tissue samples were collected from the Cancer Genome Atlas and Gene Expression Omnibus databases.Wilcoxon rank sum test and t-test were used to assess DLX gene family expression between colon cancer tissue and unpaired normal colon tissue.cBioPortal was used to analyze DLX gene family variants.R software was used to analyze DLX gene expression in colon cancer and the relationship between DLX gene family expression and clinical features and correlation heat map.The survival package and Cox regression module were used to assess the prognostic value of the DLX gene family.The pROC package was used to analyze the diagnostic value of the DLX gene family.R software was used to analyze the possible regulatory mechanisms of DLX gene family members and related genes.The GSVA package was used to analyze the relationship between the DLX gene family and immune infiltration.The ggplot2,the survminer package,and the clusterProfiler package were used for visualization.RESULTS DLX1/2/3/4/5 were significantly aberrantly expressed in colon cancer patients.The expression of DLX genes were associated with M stage,pathologic stage,primary therapy outcome,residual tumor,lymphatic invasion,T stage,N stage,age,perineural invasion,and history of colon polyps.DLX5 was independently correlated with the prognosis of colon cancer in multivariate analysis.DLX1/2/3/4/5/6 were involved in the development and progression of colon cancer by participating in immune infiltration and associated pathways,including the Hippo signaling pathway,the Wnt signaling pathway,several signaling pathways regulating the pluripotency of stem cells,and Staphylococcus aureus infection.CONCLUSION The results of this study suggest a possible role for the DLX gene family as potential diagnostic or prognostic biomarkers and therapeutic targets in colon cancer.

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Epigenetics is the study of phenotypic variations that do not alter DNA sequences.Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers.One of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer.Therefore,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences.Currently,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying loci.At each DNA methylation site,a beta value(β)is used to reflect the methylation intensity.Therefore,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user bias.This study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites.The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research Institute.The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data sets.The NBDC-GA outperformed the IGA in terms of convergence performance.Furthermore,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,respectively.The proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities that were previously linked to cancer.

Construction of a prognostic signature of m7G-related lncRNAs in bladder cancer:a bioinformatics analysis

Background:N7-methylguanosine(m7G)-related plays an important role in the occurrence and development of tumors,and some recent studies have pointed out that long non-coding RNA is involved in the occurrence and development of various cancers.However,there is no literature on how m7G-related lncRNAs predict the prognosis of bladder cancer.The purpose of this study was to develop a predictive feature based on long non-coding RNA(lncRNAs)associated with m7G to predict the prognosis of patients with bladder cancer.Methods:We obtained the RNA transcriptome data and clinical data of bladder cancer patients through the cancer genome atlas database,and obtained the lncRNAs related to m7G by co-expression analysis and Cox regression analysis.Then the signature was evaluated by receiver operating characteristic curve and nomogram,and single sample gene set concentration analysis was used to study the correlation between the predictive model and tumor immune microenvironment in high-risk and low-risk groups.Results:We got a total of 5 m7G related lncRNA(MAFG-DT,AP003352.1,AC242842.1,AC024060.1,FAM111A-DT),which may be related to the prognosis of patients with bladder cancer.For predicting 1-,3-and 5-year survival rates,the area under the receiver operating characteristic curve was 0.757 and 0.722 and 0.739,respectively.Kaplan-Meier analysis showed that the prognosis of bladder cancer patients in high risk group was worse than that in low risk group.Immunoassay showed that the immune function of patients with bladder cancer in high risk group was more active.Conclusion:Prognostic markers based on m7G-related lncRNAs can be used to independently predict the prognosis of patients with bladder cancer and provide therapeutic targets for future clinical treatment.

铜调节的细胞死亡相关基因与前列腺癌关系分析

目的基于美国癌症肿瘤基因图谱(the cancer genome atlas,TCGA)数据库分析铜调节的细胞死亡相关基因与前列腺癌患者预后和免疫细胞浸润的关系。方法从TCGA数据库下载所有前列腺癌患者的基因数据,其中包括前列腺癌组织501例,正常组织52例。运用R软件提取前列腺癌患者中铜调节的细胞死亡相关基因表达矩阵,进行差异分析、多因素回归分析筛选出预后基因,对预后基因进行生存分析,同时探讨预后相关基因与免疫细胞之间的相关性。结果甘氨酸裂解系统蛋白H(GCSH)与前列腺癌患者的预后显著相关,同时发现其与前列腺癌患者中的树突细胞、CD8^(+)T细胞、浆细胞也显著相关(P<0.05)。结论GCSH基因在前列腺癌的发生、发展中起重要作用,有望成为前列腺癌预后的标志物。

富含亮氨酸重复序列/Ⅲ型纤维连接蛋白4在胃癌组织中的表达及临床意义

目的探讨富含亮氨酸重复序列/Ⅲ型纤维连接蛋白4(leucine-rich repeat and fibronectin typeⅢdomain-containing protein 4,LRFN4)在胃癌组织中的表达情况,并分析LRFN4表达水平与胃癌患者临床病理参数和预后的关系。方法收集2017年1月至12月于中山大学附属第一医院胃肠外科中心确诊并行手术治疗的胃癌患者组织标本8对(包含胃癌组织和癌旁正常组织),并选取2004年1月至2005年12月在同一中心行手术治疗的117例胃癌患者的术后组织标本制成胃癌组织芯片。分析LRFN4在癌症基因组图谱(the cancer genome atlas,TCGA)数据库胃癌数据集中的表达情况,应用蛋白质印迹法及实时荧光定量聚合酶链反应检测LRFN4在8对新鲜胃癌组织及癌旁正常组织中的表达,应用免疫组织化学检测LRFN4在胃癌组织芯片中的表达。分析不同LRFN4表达水平的胃癌患者其临床病理参数的差异。应用Kaplan-Meier法分析不同LRFN4表达水平的胃癌患者的预后情况。应用单因素和多因素Cox回归分析法分析胃癌患者预后的影响因素。结果LRFN4在TCGA数据库胃癌数据集的胃癌组织以及新鲜胃癌组织中呈高表达状态。LRFN4高表达的患者,表现出更大的肿瘤大小以及更为进展的T分期、N分期、M分期和TNM分期(均P<0.05),其预后也较差(P<0.001)。单因素和多因素Cox回归分析提示LRFN4在胃癌组织中的高表达是影响胃癌患者预后的独立危险因素(HR=3.898,95%CI 2.273~6.686,P<0.001)。结论胃癌组织中LRFN4高表达与患者较差的预后相关,可能成为预测胃癌患者预后的生物标志物之一。

染色质调节因子对乳腺癌预后的预测价值

目的探讨染色质调节因子(CRs)在乳腺癌中的差异表达及其对预后的预测价值。方法利用癌症基因组图谱数据库筛选乳腺癌和正常乳腺组织中差异表达的CRs,并通过最小绝对值收敛和选择算子算法-Cox回归分析构建预后风险模型,根据风险分数(Risk score)将乳腺癌患者分为高风险组和低风险组;同时分析风险模型与乳腺癌患者临床特征的相关性,并对每个基因进行单独验证。结果本研究识别了127个在乳腺癌中差异表达的CRs,并基于16个关键CRs构建了预后风险模型;低风险组患者中年龄≤65岁、StageⅠ~Ⅱ期、Ⅲ~Ⅳ期、T_(0)~T_(1)、T_(2)~T_(3)、N_(0)~N_(1)、N_(2)~N_(3)者总生存期均高于高风险组(均P<0.05)。结论CRs能预测乳腺癌患者的预后。

RNA结合蛋白RBM45在肝癌中的表达及其临床意义

目的探讨核糖核酸结合基序蛋白45(RNA-binding motif protein 45,RBM45)在肝癌中的表达及其临床应用价值。方法应用癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库分析RNA结合蛋白RBM45在肝癌中的表达,并关联临床资料及病理特征进行统计学分析。运用基因富集分析软件GSEA对肝癌组织中与RBM45相关的基因进行KEGG信号通路富集分析。结果与癌旁组织相比,肝癌组织中RBM45表达明显升高(P<0.001),且RBM45表达水平与患者临床分期、肿瘤分级及预后均显著相关(P<0.05),通过构建单变量和多变量COX风险回归模型分析RBM45表达与临床资料的相关性,结果表明RBM45可做为肝癌患者独立预后的危险因素(HR:2.75695%CI:1.785-4.255,P=4.813e-06);KEGG富集分析显示,RBM45可能与细胞周期、卵母细胞减数分裂、泛素介导的蛋白水解等信号通路密切相关。结论RBM45在肝癌组织中高表达,且其高表达的患者预后较差;RBM45表达可作为肝癌患者预后判断的独立标志物。

DNMT3A/3B overexpression might be correlated with poor patient survival, hypermethylation and low expression of ESR1/PGR in endometrioid carcinoma: an analysis of The Cancer Genome Atlas

Background:DNA methylation is involved in numerous biologic events and associates with transcriptional gene silencing, playing an important role in the pathogenesis of endometrial cancer.ESR1/PGR frequently undergoes de novo methylation and loss expression in a wide variety of tumors, including breast, colon, lung, and brain tumors.However, the mechanisms underlying estrogen and progesterone receptors (ER/PR) loss in endometrial cancer have not been studied extensively.The aims of this study were to determine the expression of DNA (cytosine-5)-methyltransferase 3A/3B (DNMT3A/3B) in endometrial cancer to investigate whether the methylation catalyzed by DNMT3A/3B contributes to low ER/PR expression.Methods:The clinicopathologic information and RNA-Seq expression data of DNMT3A/3B of 544 endometrial cancers were derived from The Cancer Genome Atlas (TCGA) uterine cancer cohort in May 2018.RNA-Seq level of DNMT3A/3B was compared between these clinicopathologic factors with t-test or one-way analysis of variance.Results:DNMT3A/3B was overexpressed in endometrioid carcinoma (EEC) and was even higher in non-endometrioid carcinoma (NEEC) (DNMT3A, EEC vs.NEEC:37.6% vs.69.9%, t=-7.440, P<0.001;DNMT3B, EEC vs.NEEC:42.4% vs.72.8 %, t=-6.897, P<0.001).In EEC, DNMT3A overexpression was significantly correlated with the hypermethylation and low expression of the ESR1 and PGR (P<0.05).The same trend was observed in the DNMT3B overexpression subgroup.In the ESR1/PGR low-expression subgroups, as much as 83.1% of ESR1 and 59.5% of PGR were hypermethylated, which was significantly greater than the ESR1/PGR high-expression subgroups (31.3% and 11.9%, respectively).However, the above phenomena were absent in NEEC, while DNMT3A/3B overexpression, ESR1/PGR hypermethylation, and low ER/PR expression occurred much more often.In univariate analysis, DNMT3A/3B overexpressions were significantly correlated with worse prognosis.In multivariate analysis, only DNMT3A was an independent predictor of disease-free survival (P<0.05).Conclusions:DNMT3A/3B expression increases progressively from EEC to NEEC and is correlated with poor survival.The mechanisms underlying low ER/PR expression might be distinct in EEC vs.NEEC.In EEC, methylation related to DNMT3A/3B overexpression might play a major role in ER/PR downregulation.

白藜芦醇治疗非小细胞肺癌机制的网络药理学分析及实验验证

目的:采用网络药理学和分子对接技术识别分析白藜芦醇抗非小细胞肺癌(NSCLC)的潜在靶点和作用机制,并结合临床数据以及分子生物学实验进行初步验证。方法:通过Swiss Target Prediction数据库和Target net数据库筛选白藜芦醇靶点,通过数据库Genecards、OMIM、TTD收集NSCLC靶点,利用Venny 2.1.0平台获得药物和疾病靶点的交集;应用Cytoscape 3.7.2软件与String数据库生成靶标蛋白互作网络(PPI),并进行拓扑分析;利用Metascape数据库对交集靶标进行基因本体(GO)功能富集与京都基因与基因组百科全书(KEGG)通路富集分析,得到交集靶标的基因图谱;利用Autodock Vina软件对排名前三位的核心靶基因与白藜芦醇进行分子对接验证;通过癌症基因组图谱(TCGA)数据库获得临床病例样本,分析相关靶基因在NSCLC患者(n=1017)和健康人群(n=627)的表达情况;细胞水平采用Western Blot检测不同浓度(30、50μmol/L)白藜芦醇对人肺腺癌A549细胞SRC、EGFR及PI3K/AKT信号通路蛋白表达的影响。结果:筛选得到的潜在靶点共有40个,经过拓扑分析得到关键靶点8个,其中EGFR、SRC、ESR1等靶点与NSCLC密切相关;白藜芦醇抗NSCLC主要涉及肿瘤蛋白多糖、雌激素信号通路、PI3K/AKT等多条信号通路;分子对接显示,白藜芦醇与关键靶点具有稳定的结合能力;临床样本结果显示,靶基因EGFR、SRC、ESR1、HSP90AA1以及MMP9的表达在NSCLC中上调;TNF、CDC42以及RELA的表达在NSCLC中下调;细胞实验结果表明,白藜芦醇药物干预以剂量依赖的方式抑制了人肺腺癌A549细胞中SRC、EGFR、p-PI3K和p-AKT的蛋白表达。结论:揭示了白藜芦醇在治疗NSCLC的过程中涉及多个作用靶点及多条信号通路,明确了白藜芦醇可通过抑制PI3K/AKT信号通路发挥其抗癌效应。

基于生物信息学构建口腔鳞状细胞癌免疫基因的预后模型

目的旨在构建免疫相关基因(IRGs)的风险预测模型,以预测口腔鳞状细胞癌(OSCC)患者的预后。方法应用生物信息学技术分析OSCC的转录组测序数据,鉴定出差异表达的IRGs(DEIRGs)。通过Cox回归分析构建DEIRGs的风险预测模型,并对其预测能力进行评估。分析该模型与临床病理和免疫细胞浸润的相关性。结果通过比较OSCC和正常样本共鉴定出3634个差异表达基因,其中包括330个DEIRGs(FDR<0.05,|logFC|>1)。单因素Cox回归分析筛选出与预后相关的20个DEIRGs(P<0.05),多因素Cox回归分析筛选出其中15个DEIRGs用于构建风险预测模型。该模型可作为OSCC患者的独立预后因素(P<0.001),预测患者预后的能力具有较高的准确性(AUC=0.732),并与临床分期(t=-3.484,P<0.001)、B细胞(Cor=-0.180,P=0.002)和CD4^(+)T细胞(Cor=-0.127,P=0.026)密切相关。结论基于15个预后相关DEIRGs构建的风险预测模型能够有效地预测OSCC患者的预后,可帮助临床医生为不同风险的OSCC患者选择个性化的治疗策略。

结直肠癌p53和DNA损伤调节基因1的表达及临床意义

目的 分析p53和DNA损伤调节基因1(p53 and DNA damage regulated gene 1,PDRG1)在结直肠癌中的表达及其临床意义。方法 检索癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库中结直肠癌数据集,比较PDRG1 mRNA在结直肠癌组织和正常结直肠组织中的表达差异,分析其表达与临床病理特征及预后的关系。DNA甲基化交互可视化数据库(DNA methylation interactive visualization database,DNMIVD)分析PDRG1基因甲基化与m RNA表达水平的关系。另选取本院2019年2月至2020年5月存档的102例结直肠癌组织及其癌旁正常结直肠组织石蜡标本进行验证,采用免疫组织化学法检测PDRG1蛋白的表达。结果 TCGA数据库分析发现,PDRG1 mRNA在结直肠癌组织(n=284)中的表达较正常结直肠组织(n=41)增高(P<0.01),且结直肠癌PDRG1 mRNA表达与拷贝数变异呈正相关(n=273;r=0.792,P<0.01)。DNMIVD分析显示,PDRG1启动子甲基化β值与基因表达水平呈负相关(r=-0.34,P<0.01)。TCGA数据库分析显示,结直肠癌患者(n=273)PDRG1 mRNA表达在肿瘤位置、TNM分期及远处转移方面比较,差异均具有统计学意义(均P<0.05);对245例结直肠癌患者进行Kaplan-Meier生存分析发现,PDRG1 mRNA高表达患者(以中位数为分界值,大于中位数为高表达)无瘤生存期较短(P=0.019)。免疫组织化学检测显示,结直肠癌组织中PDRG1蛋白表达阳性率高于正常结直肠癌组织[87.3%(89/102) vs32.4%(33/102),P<0.01]。结论 PDRG1在结直肠癌中高表达,且与肿瘤位置、远处转移和无瘤生存期有关,可能成为结直肠癌潜在的生物标志物。

基于MAPK信号通路相关基因的风险评分模型预测食管鳞状细胞癌的生存及其免疫微环境与用药研究

目的:基于丝裂原活化蛋白激酶(MAPK)信号通路相关基因(MRGs)构建预后模型,提高食管鳞状细胞癌(ESCC)患者的预后效果。方法:从癌症基因组图谱(TCGA)获取ESCC患者的相关信息,从京都基因与基因组百科全书(KEGG)数据库获取MRGs。基于MRGs中与ESCC患者生存相关基因对患者进行聚类分析,对所得差异基因进行回归分析筛选预后基因构建预后模型。对该模型划分的高低风险组进行基因本体论(GO)、KEGG分析和单样本基因富集分析(ssGSEA)。利用CellMiner数据库寻找可能与模型基因相关的药物。结果:回归分析筛选出了13个基因构建了预后模型,该模型在TCGA和基因表达综合数据库(GEO)中显示出良好预测能力。GO和KEGG分析结果显示,高低风险组白细胞介导的免疫和细胞黏附分子等生物学功能和通路具有显著差异。行ssGSEA发现,高风险组患者的免疫细胞浸润和大多数免疫功能显著强于低风险组。结论:开发了一个具有良好预测能力的预后模型,可能对ESCC的预后和治疗具有指导意义。

焦亡相关基因与胃癌预后及免疫浸润的相关性分析

目的通过生物信息学筛选胃癌预后焦亡相关基因(PRGs),构建和评估风险模型并分析PRGs与免疫浸润的相关性,验证胃癌治疗的新靶点。方法从癌症基因组图谱(TCGA)数据库获取胃癌的转录组和临床数据并筛选差异表达的PRGs。采用单因素Cox回归及LASSO回归分析差异表达的PRGs并构建预后模型。Kaplan-Meier生存曲线、受试者操作特征曲线评估模型的准确性及预后价值。根据风险评分中位数将患者分为高、低风险组,Cox回归风险模型评估风险评分与预后的相关性。胃癌基因芯片数据集GSE84437作为预测模型的外部验证。RT-qPCR和Western blot检测SERPINE1、Caspase-1、NLRP3 mRNA和蛋白表达量。结果筛选35个差异表达PRGs,其中5个PRGs(CRTAC1、GPX3、IRGM、RGS2和SERPINE1)被纳入风险模型构建。年龄、M分期、N分期和风险评分是TCGA队列患者生存率的独立影响因素(P<0.05);年龄、T分期和N分期是基因表达综合(GEO)队列患者生存率的独立影响因素(P<0.05)。TCGA、GEO队列中高风险组免疫细胞浸润水平、免疫相关途径活性总体均高于低风险组(P<0.05)。CRTAC1与CD4+T细胞(r=0.488)、巨噬细胞(r=0.588)和树突状细胞(r=0.332)的浸润水平呈正相关(P<0.01);GPX3与CD4+T细胞(r=0.372)、巨噬细胞(r=0.572)和树突状细胞(r=0.406)的浸润水平呈正相关(P<0.01);RGS2与CD4+T细胞(r=0.343)、巨噬细胞(r=0.526)和树突状细胞(r=0.326)的浸润水平呈正相关(P<0.01);IRGM(r=0.327)、SERPINE1(r=0.310)与巨噬细胞的浸润水平呈正相关(P<0.01)。胃癌组织中SERPINE1表达水平高于正常胃组织(P<0.05);高表达SERPINE1组的生存时间短于低表达SERPINE1组(P<0.01)。干扰组SERPINE1、Caspase-1、NLRP3 mRNA和蛋白表达量均低于阴性对照组(P<0.05)。结论通过生物信息学方法筛选5个PRGs构建胃癌预后模型,SERPINE1高表达与胃癌不良预后相关,可能参与调控NLRP3/Caspase-1细胞焦亡途径。

亲环蛋白B在脑胶质瘤中的表达及意义

目的探讨亲环蛋白B(cyclophilin B,PPIB)在脑胶质瘤中的表达与临床病理特征、患者生存期的关系,以及与肿瘤相关巨噬细胞浸润的相关性。方法基于癌症基因组数据集(The Cancer Genome Atlas,TCGA)数据库资料,比对正常、低级别和高级别脑胶质瘤中PPIB的表达情况,采用免疫组织化学染色方法检测脑胶质瘤病理组织中PPIB的表达;基于TCGA数据库临床病例资料,分析脑胶质瘤PPIB表达与临床病理特征的关系;采用Kaplan-Meier法评估PPIB表达差异对脑胶质瘤患者生存期的影响;免疫荧光法分析脑胶质瘤病理组织中PPIB表达及其与肿瘤相关巨噬细胞浸润的相关性。结果高级别脑胶质瘤中PPIB的表达明显高于正常组织及低级别脑胶质瘤;PPIB与胶质瘤级别、IDH野生型、1p/19q非共缺失等显著相关(P<0.001);PPIB高表达患者的总生存期、无进展间隔期均低于PPIB低表达患者;除高级别脑胶质瘤病理组织中PPIB表达升高外,PPIB高表达区域伴随大量肿瘤相关巨噬细胞浸润。结论相较于低级别胶质瘤,高级别胶质瘤PPIB表达水平更高,且PPIB表达区域存在明显肿瘤相关巨噬细胞浸润。提示PPIB可能作为脑胶质瘤患者独立预后因素,为患者预后判断提供参考。

基于生物信息学构建头颈部鳞癌微小RNAs预后预测模型

目的探究头颈部鳞癌(HNSCC)中微小RNAs(miRNAs)表达情况与无复发生存期之间的相关性,并构建预后预测模型。方法利用TCGA数据库获取头颈部鳞癌的miRNAs表达谱与临床信息,通过单因素和多因素COX分析筛选出与无复发生存期相关的miRNAs,构建预后预测模型,并用受试者工作特征(ROC)曲线验证模型的准确度。通过京都基因与基因组百科全书(KEGG)和基因本体(GO)富集分析进一步探究miRNAs在细胞通路和分子功能方面的生物学意义。结果筛选出7个与预后相关的miRNAs(miR-502-5p、miR-499-5p、miR-216a-5p、miR-548e-3p、miR-203-3p、miR-6837-5p和miR-320b)用以构建模型,Kaplan-Meier结果显示高风险组患者的无复发生存期低于低风险组患者(P<0.001)。ROC曲线显示,训练集、测试集和整集的5 a生存期曲线下面积分别为0.888、0.599、0.701。基因富集分析结果提示,miRNAs与局部黏附通路、调节干细胞多能性信号通路、转化生长因子-β信号通路、Wnt信号通路、FoxO信号通路等密切相关。结论本研究构建的HNSCC miRNAs预后预测模型可有效预测患者的无复发生存期,并作为判断预后的重要指标。

基于ADME相关基因标记构建和验证子宫内膜癌预后风险评分模型

目的基于控制药物吸收、分布、代谢和排泄(ADME)过程的ADME基因,构建子宫内膜癌(UCEC)预后模型,为预测UCEC的预后及肿瘤治疗提供参考。方法从TCGA数据库和ICGC数据库中收集UCEC患者的基因表达谱以及临床数据。使用单因素Cox回归分析确定与UCEC预后相关的ADME基因,使用最小绝对收缩与选择算子(LASSO)回归筛选出最佳预后基因并构建风险评分模型。采用Kaplan-Meier生存分析和受试者工作特征曲线(ROC)评估其预测能力,以R软件筛选差异表达基因并进行功能富集分析。结果筛选出9个ADME基因(DHRS7B、CYP46A1、SLCO4C1、NR1I2、SLC16A1、SLCO3A1、ARSA、ABCC5、MGST2)用于构建UCEC预后风险评分模型。生存分析显示,低风险评分组患者的生存时间明显长于高风险评分组患者(训练集:P<0.001;验证集P=0.032)。训练集ROC曲线显示1、3和5年的曲线下面积分别为0.792、0.724和0.712,验证集分别为0.651、0.620和0.677,提示该预后风险评分模型对UCEC患者的生存状态具有良好的预测能力。单因素和多因素Cox回归分析显示,风险评分可作为UCEC潜在的独立预后因素(HR=1.77,P=0.035)。高风险评分组和低风险评分组在miRNA介导的基因沉默、调控血管内皮细胞增殖与新生血管生成和萌芽血管生成的生物学过程中存在差异。结论该研究筛选出的9个ADME基因所构建的预后风险评分模型可用于评估UCEC患者的预后。