基于TCGA数据库构建胃癌免疫相关基因的预后风险模型

目的 基于癌症基因组图谱(TCGA)数据库,通过分析差异表达的免疫相关基因构建胃癌患者的预后风险模型。方法 从TCGA数据库下载胃癌及癌旁组织的RNA测序数据和配对的患者临床资料,从Imm Port数据库中下载免疫相关基因的数据。通过比较肿瘤组织和癌旁组织,筛选出差异表达的免疫相关基因,采用Cox风险比例回归模型构建差异表达免疫相关基因的预后风险模型,通过Kaplan-Meier生存曲线和受试者工作特征(ROC)曲线下面积(AUC)评价模型的预测性能,再对模型风险评分进行独立预后分析。结果 从TCGA数据库下载了包括375例胃癌组织和32例癌旁组织的RNA测序数据,共筛选出349个差异表达的免疫相关基因,通过多因素Cox回归分析得到9个(IL1A、APOH、CGB5、GRP、TNFSF18、LGR6、MC1R、NPR1、CTLA4)与胃癌预后相关的免疫相关基因,并以此构建预后风险模型。根据模型风险评分的中位值将所有样本分为高、低风险两组,Kaplan-Meier生存分析结果显示,低风险组的总生存率高于高风险组,差异有统计学意义(P<0.001)。ROC曲线下面积为0.719,独立...     

基于TCGA构建胃癌免疫相关基因预后风险模型及相关分析

目的 对TCGA数据库胃癌数据集进行分析,构建基于免疫相关基因的预后风险模型。方法 下载TCGA数据库中胃癌组织和癌旁组织中的基因表达数据及患者相关临床资料,进行数据整理、合并、表达差异分析。与ImmPort数据库取交集获得差异表达的免疫相关基因(IRGs),并进行GO功能富集分析和KEGG通路富集分析。按照排除标准,将胃癌样本随机分为Train组(224例)、Test组(111例),利用Train组构建免疫相关基因预后风险模型并用Test组进行检验。将胃癌表达数据按照评估模型分为高、低风险2组,进行免疫浸润,分析2组免疫细胞表达水平的差异。观察2组免疫检查点表达差异及免疫治疗相关结果。结果 共得到238个差异表达的IRGs。GO功能富集分析结果示差异表达的IRGs主要参与免疫球蛋白生产、免疫反应分子介质的产生等生物学过程。KEGG通路富集分析结果示差异表达的IRGs主要参与细胞因子-细胞因子受体相互作用、神经活性配体-受体相互作用等通路。通过数据分析,得到6个IRGs(MPO APOH IGHD3-16 CGB5 GHR PRKCG)构建的预后风险模型。Train组和Test组中高风...     

基于免疫相关基因构建食管腺癌预后评分模型

目的 探讨基于免疫相关基因构建食管腺癌预后评分模型的方法和临床价值.方法 从癌症基因组图谱计划数据库下载获得食管腺癌样本及正常食管组织样本的基因表达谱以及性别、年龄、分化程度、TNM分期、生存时间、生存状态信息.使用Wilcox检验对食管腺癌样本与正常食管组织样本的免疫相关基因表达进行差异分析,继续使用Cox回归分析模...     

基于免疫相关基因的子宫内膜癌预后模型构建

目的 建立子宫内膜癌免疫相关基因预后模型,为子宫内膜癌免疫治疗提供参考.方法 从TCGA数据库下载子宫内膜癌转录组数据和临床信息,免疫相关基因列表从IMMPORT数据库获得.利用差异分析、功能富集分析评估差异免疫相关基因的功能,在训练队列中利用单因素、多因素Cox回归和最小绝对收缩和选择算法(least absolut...     

乳腺癌相关生物学预后因子的研究进展

乳腺癌是女性高发的恶性肿瘤之一,近年随着乳腺癌发病率及检出率的增高,其预后更为众人所关注。除传统判断乳腺癌预后的指标如肿瘤大小、临床分期、淋巴结转移等因素外,其它一些生物学预后因子的研究也在不断深入中,本文就目前已经确立的乳腺癌预后指标如ER、PR、HER-2 p53、Ki67等及一部分未来可能有实用价值的预后因子如nm23、MCM7、CD44、端粒酶等作一综述。     

乳腺癌相关生物学预后因子的研究进展

乳腺癌是女性高发的恶性肿瘤之一,近年随着乳腺癌发病率及检出率的增高,其预后更为众人所关注。除传统判断乳腺癌预后的指标如肿瘤大小、临床分期、淋巴结转移等因素外,其它一些生物学预后因子的研究也在不断深入中,本文就目前已经确立的乳腺癌预后指标如ER、PR、HER-2 p53、Ki67等及一部分未来可能有实用价值的预后因子如nm23、MCM7、CD44、端粒酶等作一综述。     

化生性乳腺癌预后影响因素分析与预后模型构建

目的:分析对比中国和美国化生性乳腺癌(metaplastic breast carcinoma,MBC)患者的临床病理特征和预后影响因素,并构建列线图来预测MBC患者的3年和5年生存率。方法:以SEER数据库中提取的673例患者作为建模集,采用Cox等比例回归模型分析确定MBC的独立预后因素,然后将这些因素纳入并构建列线图模型,然后以我院的36例MBC患者作为验证集进行外部验证。结果:建模集和验证集的临床病理特征除年龄、肿瘤分级、是否第一原发肿瘤及N分期外无明显差异。单因素及多因素分析结果显示,所有患者中,年龄、是否化疗、T分期、N分期以及M分期均是MBC患者预后的独立危险因素。将这些因素纳入并建立列线图预测模型。结论:列线图能准确预测我国MBC患者的预后情况,为临床的诊疗提供科学依据。     

男性乳腺癌预后影响因素分析与预后模型构建

目的 分析对比中国人民解放军空军军医大学西京医院(本院)与美国男性乳腺癌(MBC)患者的临床病理特征和预后影响因素,通过构建列线图预测MBC患者的3、5年生存率.方法 回顾性分析2006-04-01-2015-06-30本院收治的63例MBC患者的临床病理特征与预后情况;同时从美国国立癌症研究所的监测、流行病学、结果数...     

基于TCGA数据库构建肝癌铜死亡的预后模型

目的 构建肝细胞癌（HCC）患者铜死亡相关基因（CRGs）的预后模型。方法 基于TCGA数据库HCC患者的mRNA数据集,分析CRGs在HCC患者中的表达,对CRGs及相关基因进行GO和KEGG富集分析。Kaplan-Meier生存分析曲线评估CRGs的生存预后价值,分析其与免疫细胞浸润的相关性。单因素Cox回归分析筛选出与HCC患者生存预后显著相关的CRGs,Lasso回归和多因素Cox回归分析构建预后模型。根据风险值对患者进行分组并进行生存分析,ROC曲线评估预后模型,单因素和多因素Cox回归分析风险评分及临床因素与预后的关系。结果 分析得到HCC中差异表达CRGs共11个,CRGs及其相关基因主要富集的GO条目为氧化还原酶活性,作用于供体的醛基或氧基,主要富集的KEGG信号通路为碳代谢。CRGs的表达水平与浆细胞样滤泡树突细胞、T辅助细胞等免疫细胞的浸润显著相关（P<0.05）。筛选并构建3个CRGs的预后模型,包括CDKN2A、DLAT和LIPT1。高风险组和低风险组的生存时间存在显著差异（P<0.001）。风险评分是预后不良的独立危险因素（P<0.001）。...     

Development of a metabolism-related signature for predicting prognosis,immune infiltration and immunotherapy response in breast cancer

Breast cancer (BRCA) is the most commonly diagnosed cancer and among the top causes of cancer deaths globally. The abnormality of the metabolic process is an important characteristic that distinguishes cancer cells from normal cells. Currently, there are few metabolic molecular models to evaluate the prognosis and treatment response of BRCA patients. By analyzing RNA-seq data of BRCA samples from public databases via bioinformatic approaches, we developed a prognostic signature based on seven metabolic genes (PLA2G2D, GNPNAT1, QPRT, SHMT2, PAICS, NT5E and PLPP2). Low-risk patients showed better overall survival in all five cohorts (TCGA cohort, two external validation cohorts and two internal validation cohorts). There was a higher proportion of tumor-infiltrating CD8⁺ T cells, CD4⁺ memory resting T cells, gamma delta T cells and resting dendritic cells and a lower proportion of M0 and M2 macrophages in the low-risk group. Low-risk patients also showed higher ESTIMATE scores, higher immune function scores, higher Immunophenoscores (IPS) and checkpoint expression, lower stemness scores, lower TIDE (Tumor Immune Dysfunction and Exclusion) scores and IC50 values for several chemotherapeutic agents, suggesting that low-risk patients could respond more favorably to immunotherapy and chemotherapy. Two real-world patient cohorts receiving anti-PD-1 therapy were applied for validating the predictive results. Molecular subtypes identified based on these seven genes also showed different immune characteristics. Immunohistochemical data obtained from the human protein atlas database demonstrated the protein expression of signature genes. This research may contribute to the identification of metabolic targets for BRCA and the optimization of risk stratification and personalized treatment for BRCA patients.     

基于NR3C2相关免疫调节基因的乳腺癌预后模型构建与验证

目的通过生物信息学方法分析NR3C2基因在乳腺癌中的免疫作用并构建预后模型。 方法(1)分别以癌症基因组图谱(TCGA)数据库中的乳腺癌队列和基因综合表达(GEO)数据库中的GSE42568队列作为训练集(113例癌旁样本和1 019例乳腺癌样本)和测试集(17例癌旁样本和104例乳腺癌样本),比较上述2个队列中NR3C2在癌旁样本和乳腺癌样本中的mRNA表达;通过TCGA队列、Kaplan-Meier plotter队列(4 929例乳腺癌样本)分析NR3C2表达对无复发生存期(RFS)的影响。(2)利用基因集富集分析(GSEA)探讨NR3C2潜在的生物学功能,通过单样本基因集富集分析(ssGSEA)定量评估24种免疫细胞,以皮尔森系数计算NR3C2和24种免疫细胞及70个免疫调节基因的相关性。(3)通过多元逐步Cox回归的方法在TCGA队列中构建NR3C2相关免疫调节基因的预后模型,按照中位风险值将TCGA队列分为高、低风险组,比较2组的无复发生存率,采用受试者操作特征曲线(ROC)计算模型的敏感度和特异度,并在GSE42568队列中进行验证;结合其他临床参数,通过多因素Cox回归分析该模型的独立预后性能。(4)在TCGA队列中,基于临床分期和风险值构建列线图,利用校准曲线对其准确性进行评价,通过时间相关曲线下面积(tAUC)比较不同指标预测的准确度。(5)为了验证NR3C2基因在mRNA和蛋白水平上的表达是否一致,本研究另外收集了2021年9月于陆军特色医学中心乳腺甲状腺外科进行手术切除的3例乳腺癌患者的临床组织样本,通过Western blot实验检测其癌旁组织和癌组织中NR3C2的蛋白表达量。 结果(1)TCGA样本分析结果显示：与癌旁组织相比,NR3C2的mRNA表达量在乳腺癌组织中显著下降(2.59±0.43比0.98±0.62,t＝35.990,P<0.001)。在GSE42568队列中,乳腺癌组织中NR3C2的mRNA表达量比癌旁组织显著下降(5.35±1.47比3.32±1.12,t＝7.096,P<0.001)。生存分析结果显示：在TCGA队列、Kaplan-Meier plotter队列中,NR3C2表达和乳腺癌患者的RFS呈正相关(HR＝0.667、0.725,95%CI：0.458~0.972、0.653~0.804,P均<0.050)。(2)GSEA结果提示：NR3C2主要参与JAK-STAT和TGF-β等免疫相关信号通路。相关性分析发现：NR3C2的mRNA表达和19种免疫细胞的浸润程度及43个免疫调节基因的表达均显著相关(P均<0.050)。(3)将上述43个NR3C2相关的免疫调节基因纳入Cox回归分析,构建了13个免疫调节基因组成的预后模型,风险截断值为0.988。生存分析提示在TCGA队列及GSE42568队列中,高风险组的RFS明显低于低风险组(HR＝2.682、2.389,95%CI：1.839~3.910、1.343~4.248,P均<0.010);AUC为0.758、0.618(95%CI：0.662~0.857、0.545~0.758,敏感度：0.833、0.538,特异度：0.614、0.714,P均<0.010)。多因素Cox回归分析发现该模型的风险值可作为乳腺癌独立的预后因子(HR＝1.259、1.163,95%CI：1.187~1.336、1.068~1.266,P均<0.001)。(4)基于临床分期和风险值构建的列线图可以预测乳腺癌患者3年、5年和8年的RFS,校准曲线提示其具有较好的预测准确性,tAUC提示其优于临床分期和预后模型。(5)Western blot实验结果显示：NR3C2的蛋白表达量在乳腺癌组织中显著降低。 结论NR3C2是乳腺癌患者潜在的免疫治疗靶点和预后生物标志物。     

Aneuploidy as a prognostic factor in breast cancer

The predictive value of nuclear DNA content in mammary carcinoma is still under debate in spite of several reports indicating a relationship between DNA ploidy and prognosis. The impact of differences in methodology on the evaluation of DNA data is discussed, and a recent study demonstrating DNA ploidy as a statistically significant prognostic variable on a prospective material of breast cancer patients is presented.     

乳腺癌中基因标签研究的共性问题

基冈标签的研究共性问题包括:①临床验证,包括各模型在独立资料中的验证、多个模型在同一组资料中共同验证、两个重要的前瞻性验证研究的基本信息;②在判断乳腺癌患者预后、判断诱导化疗疗效和指导术后辅助化疗方面的价值;③与传统的临床病理学评价体系比较的优缺点.     

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

BackgroundThere is accumulating evidence that autophagic activity is crucial to the development of hepatocellular carcinoma (HCC). Thus, we sought to develop a predictive model based on autophagy-related genes (ARGs) to forecast the prognosis of HCC patients.MethodsBased on expression data from The Cancer Genome Atlas (TCGA) and ARGs from Human Autophagy Database (HADb), the differentially expressed ARGs were screened. The prognosis-related ARGs were identified using a univariate Cox regression analysis. Using multivariate Cox regression analysis, a prognostic model was developed. To assess the predictive value of the model, receiver operating characteristic (ROC) curve, Kaplan-Meier curve, and multivariable Cox regression analyses were conducted. A data cohort gathered independently from the International Cancer Genome Consortium (ICGC) database further verified the model’s predictive accuracy. The immune landscape was generated using the TIMER and CIBERSORT algorithms. Finally, the correlation between the prognostic signature and gene mutation status was analyzed by employing “maftools” package.ResultsWe identified a novel prediction model based on the ARGs of PLD1 and SLC36A1 with significant prognostic values for HCC in both univariate and multivariate Cox regression analysis, and patients were classified into high- or low-risk groups based on their risk scores. High-risk patients had significantly shorter overall survival (OS) times than low-risk patients (P=5e-4). According to the ROC curve analysis, the risk score had a higher predictive value than the other clinical characteristics. Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with HCC. Further, an external independent cohort of ICGC patients provided additional confirmation of the predictive efficacy of the model. We subsequently analyzed the differential immune densities of the two groups and discovered that various immune cells, including naïve B cells, resting memory cluster of differentiation (CD)4 T cells, regulatory T cells, M2 macrophages, and neutrophils, had considerably larger infiltrating densities in the high-risk group than the low-risk group.ConclusionsWe established a robust autophagy-related risk model having a certain prediction accuracy for predicting the prognosis of HCC patients. Our findings will contribute to the definition of prognosis and establishment of personalized treatment interventions for HCC patients.     

An inflammatory-related genes signature based model for prognosis prediction in breast cancer

Background: Breast cancer has become the most common malignant tumor in the world. It is vital to discover novel prognostic biomarkers despite the fact that the majority of breast cancer patients have a good prognosis because of the high heterogeneity of breast cancer, which causes the disparity in prognosis. Recently, inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer, so we set out to investigate the predictive usefulness of inflammatory-related genes in breast malignancies. Methods: We assessed the connection between Inflammatory-Related Genes (IRGs) and breast cancer by studying the TCGA database. Following differential and univariate Cox regression analysis, prognosis-related differentially expressed inflammatory genes were estimated. The prognostic model was constructed through the Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs. The accuracy of the prognostic model was then evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. The nomogram model was established to predict the survival rate of breast cancer patients clinically. Based on the prognostic expression, we also looked at immune cell infiltration and the function of immune-related pathways. The CellMiner database was used to research drug sensitivity. Results: In this study, 7 IRGs were selected to construct a prognostic risk model. Further research revealed a negative relationship between the risk score and the prognosis of breast cancer patients. The ROC curve proved the accuracy of the prognostic model, and the nomogram accurately predicted survival rate. The scores of tumor-infiltrating immune cells and immune-related pathways were utilized to calculate the differences between the low- and high-risk groups, and then explored the relationship between drug susceptibility and the genes that were included in the model. Conclusion: These findings contributed to a better understanding of the function of inflammatory-related genes in breast cancer, and the prognostic risk model provides a potentially promising prognostic strategy for breast cancer.     

Integrative multiomics analysis identifies a metastasis-related gene signature and the potential oncogenic role of EZR in breast cancer

Distant metastasis is a major cause of increased mortality in breast cancer patients, but the mechanisms underlying breast cancer metastasis remain poorly understood. In this study, we aimed to identify a metastasis-related gene (MRG) signature for predicting progression in breast cancer. By screening using three regression analysis methods, a 9-gene signature (NOTCH1, PTP4A3, MMP13, MACC1, EZR, NEDD9, PIK3CA, F2RL1 and CCR7) was constructed based on an MRG set in the BRCA cohort from TCGA. This signature exhibited strong robustness, and its generalizability was verified in the Metabric and GEO cohorts. Of the nine MRGs, EZR is an oncogenic gene with a well-documented role in cell adhesion and cell migration, but it has rarely been investigated in breast cancer. Based on a search of different databases, EZR was found to be significantly more highly expressed in both breast cancer cells and breast cancer tissue. EZR knockdown significantly inhibited cell proliferation, invasion, chemoresistance and EMT in breast cancer. Mechanistically, RhoA activation assays confirmed that EZR knockdown inhibited the activity of RhoA, Rac1 and Cdc42. In summary, we identified a nine-MRG signature that can be used as an efficient prognostic indicator for breast cancer patients, and owing to its involvement in regulating breast cancer metastasis, EZR might serve as a therapeutic target.     

Immunohistochemical detection of P-glycoprotein in breast cancer and its significance as a prognostic factor

Overexpression of P-glycoprotein (Pgp) in tumors is one of the major mechanisms which mediates the multidrug resistance (MDR) phenotype. To evaluate the prognostic significance of Pgp in breast cancer, Pgp expression was examined in paraffin-embedded tissue sections of 94 breast cancer specimens by immunohistochemistry. Tissue specimens were obtained by mastectomy without preoperative chemotherapy. UIC2 monoclonal antibody which recognizes an extracellular epitope of human Pgp was employed. Of the 94 breast cancer specimens, 35 (37.2%) were positive for Pgp expression. Pgp expression had no correlation with menopausal or hormone receptor status, axillary lymph node involvement or tumor size. However, a significant correlation was observed between Pgp expression and disease relapse (p = 0.0322). Pgp-positive patients showed a significantly shorter disease-free survival period than Pgp-negative patients by the Kaplan-Meier method (p = 0.0433). These results suggest that immunohistochemical detection of Pgp in breast cancer tissue may have prognostic value after radical operation.