基于TCGA数据库建立的八基因预后模型在乳腺癌中的应用

目的 利用TCGA数据库建立预测乳腺癌预后的多基因预后模型， 分析多基因预后模型与乳腺癌各临床病理特征之间的关系。方法 对TCGA数据库中乳腺癌患者的mRNA数据进行整理， 通过R语言软件筛选出在乳腺癌样本及正常样本中差异表达的基因， 采用Cox比例风险回归模型从中筛选和建立多基因预后模型， 计算预后评分。根据预后评分的中位数将患者分为高风险组和低风险组。将临床病理因素和预后评分因素纳入Cox回归模型分析乳腺癌患者的生存影响因素。根据年龄、 ER受体状态、 HER-2表达情况、 淋巴结转移状态及病理分期进行分组， 采用Kaplan-Meier （K-M） 方法以多基因预后模型作为影响因素进行生存分析， 验证多基因模型对总体及各亚组乳腺癌患者中的预后价值。结果 将分析得到的2 142个差异基因纳入Cox回归分析， 共筛选出8个差异基因， 包括羧基酯脂肪酶 （CEL）、 POU区域转录因子 （POU3F2）、 维生素D-24羟化酶 （CYP24A1）、 脂肪酸结合蛋白7 （FABP7）、 MURC、 G 蛋白偶联受体 （GCCR）、 低密度脂蛋白受体相关蛋白-1B （LRP1B） 及丝氨酸蛋白酶2 （PRSS2）， 并建立八基因预后模型。预后评分 （PI） 公式为： PI=0.156×CEL的表达量+0.112×POU3F2的表达量-0.071×CYP24A1的表达量-0.065× FABP7的表达量+0.135×MURC的表达量-0.201×GCGR的表达量-0.063×LRP1B的表达量-0.090×PRSS2的表达量。计算709例患者预后评分后， 中位值为0.98， 共有355例患者纳入高风险组， 354例患者纳入低风险组。Cox回归分析显示， 年龄、 病理分期和八基因预后模型均是乳腺癌患者预后的独立影响因素 （P＜0.05）。生存分析证实， 在总体乳腺癌患者及各亚组 （除Ⅳ期外） 乳腺癌患者中， 预后评分低风险的患者总体生存率明显升高， 差异有统计学意义 （P＜ 0.01）。结论 八基因预后模型可用于预测乳腺癌患者的预后， 在根据临床病理特征分组的乳腺癌亚组中得到了验证， 有利于进一步指导临床治疗。     

肝细胞癌免疫相关基因和 lncRNA联合预后模型的构建及验证

目的构建肝细胞癌(HCC)免疫相关的基因( IRGs)和 lncRNA(IRlncRNAs)联合预后模型。方法 2022年 6—8月通过 TCGA数据库下载 HCC转录组及临床数据;对转录组中 IRGs进行加权基因共表达网络分析( WGCNA)得到与预后相关的核心基因,对核心基因与转录组中 lncRNA共表达分析得到 IRlncRNAs;单因素 Cox回归筛选生存相关的 IRGs和 IRlncRNAs,LASSO回归构建模型并进行验证;对高低风险病人差异表达的基因进行 GO(基因本体论)和 KEGG(京都基因与基因组百科全书)分析探索影响预后的可能机制。结果构建了由 6个 IRGs及 7个 IRlncRNAs组成的预后风险模型;不同风险病人组织学分级(P =0.001)、临床分期(P=0.005)、 T分期( P=0.010)差异有统计学意义;在训练集、测试集和总样本集中,高风险病人总生存期较低风险病人显著降低(均 P<0.05);模型在预测 HCC病人 1年生存率中表现良好,训练集、测试集和总样本集受试者操作特征(ROC)曲线下面积分别为 0.85、0.81和 0.83;多因素 Cox回归表明评分可独立于其他特征预测病人生存( P<0.001); GO分析显示差异基因主要参与有丝分裂等事件; KEGG分析显示差异基因参与了磷脂酰肌醇 3激酶 -蛋白激酶 B、细胞周期等通路。结论基于 IRGs和 IRlncRNAs构建的 HCC预后模型具有较好的预测价值,可能有助于 HCC的临床决策和管理。     

基于TCGA筛选结肠癌预后相关lncRNA及建立预后风险模型

目的　筛选与结肠癌预后相关长链非编码RNA（lncRNA）,并构建结肠癌预后风险模型。方法　数据提取时间：建库至2022年3月1日。从癌症基因组图谱（TCGA）数据库下载并整理结肠癌转录组数据,构建配对样本lncRNA表达矩阵,利用“edgeR”R包筛选获得差异表达lncRNA（DElncRNA）。对DElncRNA先后行COX回归模型单变量分析、Lasso回归分析、Kaplan-Meier（K-M）生存分析、多元COX回归模型分析,获取预后相关lncRNA。依据多元COX回归模型中回归系数构建结肠癌预后风险模型。通过C指数值、时间依赖的受试者工作特征曲线（ROC）和ROC下的面积（AUC）及K-M生存分析评估模型预测的准确性。对模型中lncRNA构建竞争性内源RNA（ceRNA）网络,对相关的mRNA进行基因本体论（GO）、京都基因与基因组大百科全书数据库（KEGG）富集分析,探索lncRNA影响结肠癌进展的机制。结果　整理转录组数据得到5 460个lncRNA,配对样本分析获得DElncRNA 868个,其中上调548个、下调320个。单变量COX回归分析后获得40个lncRNA,经Lasso回归分析过滤共线性因素,得到lncRNA 34个,K-M生存分析后,得出14个候选lncRNA。再进行多元COX回归分析,得到7个预后相关lncRNA（下调：LINC01132;上调：ELFN1-AS1、RP5-884M6.1、LINC00461、RP1-79C4.4、RP4-816N1.7、RP3-380B8.4）,依据回归系数构建预后风险模型。模型的C指数值为0.82;3年和5年的AUC值分别为0.79、0.84;进行K-M生存分析提示高低风险组生存率差异有统计学意义（P<0.000 1）。随后构建ceRNA网络,通过KEGG富集分析提示下调lncRNA可能是通过肌动蛋白细胞骨架的调控、癌症中蛋白聚糖、PI3K-Akt信号通路等抑制结肠癌进展,上调lncRNA可能是通过细胞粘附分子、局灶性粘连、吞噬体等通路促进结肠癌进展。结论　本研究构建了一个包含7个lncRNA的结肠癌预后风险模型,具有较好预测患者生存预后准确性,每个lncRNA是潜在单独的预后生物标志物,对临床上结肠癌患者预后评估具有一定参考价值。     

胃癌肿瘤大小的临床病理特征及预后分析(附753例报告)

目的 探讨不同肿瘤大小直径组胃癌的临床病理特征及预后差异.方法 对753例胃癌患者施行胃癌D2根治术,应用cox比例风险模型对肿瘤大小进行最佳截点的筛选.对全组胃癌患者的预后因素进行单因素及多因素分析,并对大直径组(LSG)及小直径组(SSG)胃癌患者的预后因素进行多因素分析.结果 全组胃癌患者通过Cox比例风险模型筛选出肿瘤大小的最佳截点为5 cm.肿瘤≥5 cm的患者(大直径组)333例(44.2％),＜5 cm患者(小直径组)420例(55.8％);肿瘤大小与位置、Borrmann分型、分化程度、神经浸润、脉管癌栓、浸润深度、淋巴结转移、病理分期有关(P＜0.05).大直径组和小直径组术后5年生存率分别为39.1％和74.5％ (P＜0.05).通过Cox比例风险模型分析显示,肿瘤大小、位置、TNM分期、浸润深度、淋巴结转移、脉管癌栓、神经浸润是影响全组患者预后的独立因素(均P＜0.01).小直径组胃癌组5年生存率(74.5％)低于大直径组5年生存率(39.1％,P＜0.01).结论 肿瘤大小是影响胃癌患者预后的独立因素,可能作为术后患者辅助化疗的依据.     

基于坏死性凋亡相关长链非编码RNA构建肝细胞癌预后模型及药物治疗反应分析

目的 使用坏死性凋亡相关长链非编码RNA(NRLs)构建肝细胞癌（HCC）预后模型并分析不同风险组间药物敏感性差异,为HCC病人预后预测和临床个体化治疗提供理论依据。方法 从癌症基因组图谱（TCGA）数据库中下载HCC病人的RNA测序数据和临床信息。采用共表达网络分析鉴定NRLs。使用单变量Cox回归和LASSO-Cox回归构建预后模型,并在测试集和整个集合中进行验证。运用生存分析、受试者操作特征（ROC）曲线、临床病理分层相关性分析、多变量Cox回归、列线图和校准曲线来评估预后模型。随后,采用基因集富集分析（GSEA）不同风险群体间生物过程和功能的差异。使用单样本基因集富集分析（ssGSEA）来探讨不同风险群体与肿瘤免疫、浸润之间的关系,并采用Pearson相关分析HCC病人预后特征与免疫检查点表达的相关性。最后,使用药物敏感性分析20种化疗药物在不同风险群体中的IC50值。结果 构建了由4个NRLs(ZFPM2-AS1、MKLN1-AS、LINC01116、AP003390.1)组成的风险评分（NRLs risk-Score）预后特征,并根据风险评分中位值将病人划分为高风险组和低风...     

膀胱癌m6A调节因子预后模型建立与分析

目的 分析m6A调节因子对膀胱癌（bladder cancer, BC）预后的影响,建立预后预测模型。方法 从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库获取397例BC组织的高通量测序数据和对应的临床病理特征数据。在26个m6A调节因子中,采用单因素Cox回归筛选预后相关的m6A调节因子,利用最小绝对值收敛和选择算子（least absolute shrinkage and selection operator, LASSO）Cox回归分析方法构建BC预后预测模型,比较高低风险组总生存期（overall survival, OS）、免疫检查点相关基因和靶向治疗相关基因表达的差异。通过基因集富集分析比较高低风险组中信号通路的富集情况,采用单样本基因富集分析（single sample gene set enrichment analysis,ssGSEA）和估计恶性肿瘤组织中基质和免疫细胞（estimation of stromal and immune cells in malignant tumors using expression data...     

胶质母细胞瘤铜死亡相关免疫检查点基因特征及潜在候选药物分析

目的 综合运用生物信息学方法进行胶质母细胞瘤铜死亡相关免疫检查点基因(ICGs)特征分析及潜在候选药物预测。方法 从癌症基因组图谱(TCGA)数据库下载胶质母细胞瘤患者m RNA表达和临床信息。采用Cox回归、Lasso回归及交叉验证筛选预后相关的ICGs,构建风险模型并对胶质母细胞瘤患者进行风险评分。利用风险评分及临床特征构建列线图预测模型,通过校准曲线及受试者特征曲线(ROC)曲线进行验证。利用Kaplan-Meier曲线对高、低风险组进行预后分析。利用DSigDB数据库筛选ICGs相关的潜在候选药物,并对高、低风险组药物敏感性进行分析。结果 共得到36个铜死亡相关的ICGs。通过Cox回归、Lasso回归筛选出4个预后相关ICGs,分别是CD276、TNFSF14、CD40、TNFSF9。多因素Cox回归结果示,风险评分是胶质母细胞瘤患者的独立预后因子。ROC曲线图预测1、3年生存率的ROC曲线下面积分别为0.726(95%CI:0.637～0.816)、0.731(95%CI:0.593～0.870)。GSEA分析结果显示,DNA复制、间隙连接、糖胺聚糖生物合成等癌症通路主要富...     

中性粒细胞/淋巴细胞比率与转移性胃癌患者预后的关系

目的　研究中性粒细胞/淋巴细胞比率（NLR）与转移性胃癌患者预后的关系。方法　回顾性分析148例转移性胃癌患者的临床病理资料,根据NLR中位数将患者分为高NLR组（NLR≥2.51,73例）与低NLR组（NLR＜2.51,75例）。比较2组患者的临床病理特点和总生存率,Cox多因素分析确定影响转移性胃癌总生存率的危险因素,Kaplan-Meier法绘制2组患者的生存曲线。结果　高NLR组转移部位≥2、肝脏转移、腹膜转移、幽门螺旋杆菌感染及癌胚抗原（CEA）≥5 μg/L比例明显高于低NLR组（P＜0.05）。生存分析结果显示,高NLR组的生存时间明显短于低NLR组（10个月vs. 22个月,Log-rank χ2=4.125,P＜0.05）。高NLR组患者随访1、3、5年的总生存率明显低于低NLR组患者（26.03% vs. 65.33%、6.85% vs. 16.00%、0 vs. 4.00%,均P＜0.05）。Cox多因素分析结果表明肝脏转移、腹膜转移及NLR≥2.51均是影响转移性胃癌患者总生存率的独立危险因素。结论　NLR可以预测转移性胃癌患者的远期生存情况,NLR≥2.51的患者远期生存较差。     

CDH1基因甲基化状态与结直肠癌临床病理特征及预后的相关性分析

目的 检测原发性结直肠癌患者术中腹腔灌洗液悬浮细胞中的CDH1基因启动子所在5''-CpG岛的异常甲基化,同时对其异常甲基化与临床病情发展、病理变化及术后预后的相关关系进行探讨。 方法 选取该院肿瘤科进行结直肠癌切除术的原发性结直肠癌患者,所有入选患者由专人进行跟踪随访。检测CDH1基因启动子所在5''-CpG岛的异常甲基化,对其异常甲基化与临床病情发展、病理变化及术后预后的相关关系进行探讨。 结果 该研究共纳入患者184例,其中甲基化组86例,非甲基化组98例。对两组患者临床病理结果检查可知甲基化组肿瘤直径大于非甲基化组(P＜0.001),甲基化组肿瘤浸润性所占比例高于非甲基化组(P＜0.001),甲基化组肿瘤分化程度低于非甲基化组(P＜0.001),甲基化组淋巴转移率、远处转移率高于非甲基化组(P＜0.001,P=0.026),甲基化组临床TNM分期更晚(P＜0.001)。根据随访结果显示非甲基化组患者生存率高于甲基化组患者(P＜0.05)。Cox比例风险模型结果显示,患者肿瘤的大体分型、分化程度、侵袭程度和病理分期是影响生存预后的变量,其中腹腔灌洗液悬浮细胞CDH1基因甲基化是影响预后最重要的独立因素,RR=28.514。 结论 原发性结直肠癌患者腹腔灌洗液悬浮细胞CDH1基因甲基化程度提高,恶性程度高,预后差。     

外周血中性粒细胞和淋巴细胞比值、营养指数及体质量指数与胃癌预后关系

目的 探讨外周血中性粒细胞和淋巴细胞比值(NLR)、预后营养指数(PNI)、体质量指数(BMI)在胃癌根治术病人预后评价中的意义,为胃癌病人的临床诊治提供新思路。方法 回顾性分析芜湖市第二人民医院2009年1月至2012年7月143例胃癌病人的临床病理资料,分析NLR、PNI及BMI与临床病理特征之间的关系。采用Kaplan-Meier法绘制术后生存曲线和估计中位生存时间,用多重Cox等比例风险模型分析影响生存时间的因素。结果 NLR与病人性别相关(86.67%比71.43%,P＜0.05),PNI与病人神经侵犯状况有关(54.55%比35.23%,P＜0.05),BMI与病人年龄(77.63%比38.81%,P＜0.05)及CEA高低(68.09%比42.86%,P＜0.05)相关。单因素Kaplan-Meier分析表明NLR、PNI和BMI均与病人预后有关,但是多重Cox等比例风险模型分析表明仅PNI影响病人预后,低PNI病人死亡风险是高PNI病人死亡风险的1.751倍(95%CI:1.189～2.578)。结论 PNI是胃癌预后的独立因素,为临床胃癌病人的诊治提供了新的思路。     

Increased expression of TTC21A in lung adenocarcinoma infers favorable prognosis and high immune infiltrating level

BackgroundLung adenocarcinoma (LUAD) is a crucial pathological type of lung cancer. Immune-infiltration of the tumor microenvironment positively associated with overall survival in LUAD. TTC21A is a gene has not reported in cancer, and the mechanism behind it is still unclear. Our study assesses TTC21A role in LUAD, via TCGA data.MethodsGEPIA was utilized to analyze the expression of TTC21A in LUAD. We evaluated the influence of TTC21A on survival of LUAD patients by survival module. Then, data sets of LUAD were downloaded from TCGA. The correlations between clinical information and TTC21A expression were analyzed using logistic regression. Clinicopathologic characteristics associated with overall survival in TCGA patients using Cox regression. In addition, we explored the correlation between TTC21A and cancer immune infiltrates using CIBERSORT and “Correlation” module of GEPIA.ResultsThe univariate analysis using logistic regression, wherein TTC21A expression served as a categorical dependent variable (with a median expression value of 2.5), indicated that increased TTC21A expression is significantly correlated with pathological stage, tumor status and lymph nodes. Moreover, multivariate analysis revealed that the up-regulated TTC21A expression, negative results of pathological stage and distant metastasis are independent prognostic factors for good prognosis. Specifically, a positive correlation between increased TTC21A expression and immune infiltrating level of B cells, Neutrophils, Mast cells and T cells was established using CIBERSORT analysis. Furthermore, we confirmed it in “correlation” module of GEPIA.ConclusionTogether with all these findings, increased TTC21A expression correlates with favorable prognosis and increased proportion of immune cells, such as B cells, Neutrophils, Mast cells and T cells in LUAD. These conclusions indicate that TTC21A could serve as a potential biomarker to assess prognosis and immune infiltration level in LUAD.     

A robust signature associated with patient prognosis and tumor immune microenvironment based on immune-related genes in lung squamous cell carcinoma

BackgroundLung squamous cell carcinoma (LUSC) is one common type of lung cancer. Immune-related genes (IRGs) are closely associated with cancer prognosis. This study aims to screen the key genes associated with LUSC and establish an immune-related prognostic model.MethodsBased on the Cancer Genome Atlas (TCGA) database, we screened the differentially expressed genes (DEGs) between LUSC and normal samples. Intersecting the DEGs with the immune-related genes (IRGs), we obtained the differentially expressed IRGs (DEIRGs). Univariate as well as multivariate Cox regression analyses were performed to identify the survival-associated IRGs and establish an immune-related prognostic model. The relationship between the prognostic model and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT.ResultsA total of 229 DEIRGs were screened, and 14 IRGs associated with survival were identified using univariate Cox analysis. Among the 14 IRGs, six genes were selected out using Lasso and multivariate Cox analyses, and they were used to build the prognostic model. Further analysis indicated that overall survival (OS) of high-risk groups was lower than that of low-risk groups. High risk score was independently related to worse OS. Moreover, the risk score was positively correlated with several immune infiltration cells. Finally, the efficacy of the prognostic model was validated by another independent cohort GSE73403.ConclusionThe DEIRGs described in the study may have the potential to be the prognostic molecular markers for LUSC. In addition, the risk score model could predict the OS and provides more information for the immunotherapy of patients with LUSC.     

Development of a novel prognostic signature based on antigen processing and presentation in patients with breast cancer

OBJECTIVE Complex immune processes are involved in the development and progression of breast cancer(BRCA). As a single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer, there has been no investigation to find a robust signature to predict the survival outcome of BRCA patients in the aspect of tumor immunology. We aimed to develop a more accurate gene signature based on immune related genes for prognosis prediction of BRCA. METHODS The information of BRCA patients obtained from The Cancer Genome Atlas(TCGA). Gene Set Enrichment Analysis(GSEA) confirmed the gene set of antigen processing and presentation contributed to BRCA. Cox proportional regression, multivariate Cox regression and stratified analysis identified prognostic power of the gene signature. Differentially expressed m RNAs between high-and low-risk groups were analyzed by KEGG. Molecular docking studies were carried out using MOE(Molecular Operating Environment) software.RESULTS The three-gene signature heat shock protein family A member 5(HSPA5), proteasome activator subunit2(PSME2), major histocompatibility complex class I, F(HLA-F) were significantly associated with overall survival(OS). HSPA5, PSME2 were protective type with HR<1 and HLA-F was risky type with HR>1. Risk score, age,race, pathological stage and cancer status were independent prognostic indicators. KIT and ACACB may paly important role in the mechanism of gene signature regulates the prognosis of BRCA. Octanoic acid may be the optimum molecular binding to HLA-F. CONCLUSION The proposed three-gene signature is a promising biomarker for estimating survival outcome in BRCA patients.     

Identification of novel epithelial-mesenchymal transition-related gene features to predict survival outcome in patients with pancreatic cancer

OBJECTIVE To find a novel genetic marker to improve the prediction of PC metastasis is urgently needed. In 2018, pancreatic cancer caused the seventh mostly common cancer-death in the world. The mortality rate of pancreatic cancer is closely related to the incidence, making PC the deadliest cancer. Most of the existing biomarkers related to the survival and prognosis of mining are single-gene biomarkers, but its prediction effect is not specific enough. METHODS GSEA(http://www.broadinstitute.org/gsea/index.jsp) was conducted to explore whether the identified sets showed significant differences between the two groups. We used Univariate Cox regression analysis to identify genes that related to OS with P<0.05 and multivariate Cox proportional hazards regression analysis to further confirm the prognostic genes from the previous step. Subsequently, we established a prognostic risk score formula. RESULTS We analyzed the m RNA expression data collected from 171 PC patients from the TCGA database and obtained 197 m RNA significantly associated with epithelial-mesenchymal transition by gene set enrichment analysis(GSEA). We built a Cox proportional regression model and obtained a set of genes that are remarkably associated with overall survival(OS) and lymph node metastasis in the test series. Multivariate Cox regression analysis was used to figure out a three-gene signature and to calculate the risky score. Based on this, patients in the test series can be divided into high-risk or low-risk subgroups. The prognostic ability of the three gene features was not affected by clinical features. CONCLUSION Therefore, the set of genetic-marker combinations related to epithelial-mesenchymal transition is able to predict the prognosis of lymph node metastasis in PC. It provides a new idea for understanding the epithelial-mesenchymal transition and the prognosis of PC metastasis.     

基于TCGA数据库的胆囊癌关键基因及预后基因的挖掘与分析

目的 通过TCGA数据库深入挖掘胆囊癌发生的关键基因,寻找胆囊癌的预后基因。方法 从癌症基因组图谱（TCGA）数据库中下载胆囊癌及癌旁正常组织转录组数据,采用R软件中的edgeR包对数据进行差异表达分析,将获取的差异表达基因进行GO和KEGG富集分析,并通过STRING在线生物信息学工具构建蛋白质-蛋白质相互作用（PPI）网络,通过Cytoscape软件进行关键基因筛选,利用R软件中的survival包对关键基因进行生存预后分析。结果 共获取胆囊癌差异表达基因1 766个,其中上调基因1 172个,下调基因594个。差异基因主要富集于环氧酶P450途径、细胞器膜、四烯酸环氧酶活性和代谢途径。构建PPI网络,获取10个关键基因,分别是BUB1、BUB1B、CDK1、UBE2C、KIF2C、AURKB、CDC20、KIF23、CCNB2和KIF20A。生存分析显示,KIF23与胆囊癌的预后显著相关。结论 基于TCGA数据库挖掘出10个胆囊癌关键基因,有助于深入了解胆囊癌的发生发展过程,KIF23有可能成为胆囊癌潜在的治疗靶点及预后标志物。     

Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model

Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.     

基于对枸杞、姜黄、甘草的生物信息学分析探究GRIN3A在前列腺癌中的治疗意义

目的综合分析枸杞、姜黄、甘草在前列腺癌中的治疗作用,发掘新的靶向治疗基因。方法BATMAN-TCM数据库预测3类药材靶向蛋白基因,使用TCGA数据库筛选出差异表达基因。在Cytoscape中选择候选核心基因,通过生存分析确定最终核心基因。对核心基因进行单基因GSEA分析以及COX回归分析。结果3类药材共同靶向基因180个,其中在TCGA数据库前列腺肿瘤组织与正常组织间差异表达的基因有11个。Cytoscape中筛选出候选差异基因PTGS2、GRIN3A、CACNA1D。生存分析显示,仅GRIN3A与患者不良预后相关。单基因GSEA提示,GRIN3A相关的生物学活动对前列腺癌的发生发展具有促进作用。单因素及多因素COX回归提示,GRIN3A为疾病的独立预后因素。结论GRIN3A在枸杞、姜黄、甘草对前列腺癌的治疗机制中发挥重要作用,该基因具有成为前列腺癌靶向治疗位点的潜能。     

Vav1在胃癌中的表达及其与预后的相关性

目的 研究胃癌中 Vav1 的表达情况, 分析 Vav1 表达与胃癌临床病理因素及预后的关系。 方法 实时定量 PCR 检测并比较胃癌细胞株（HGC-27、SGC7901、MGC803）及正常胃黏膜细胞株（GES-1）中 Vav1 mRNA 的表达,免疫组化检测 105 例胃癌病理组织石蜡标本中 Vav1 蛋白的表达情况, 分析胃癌患者临床病理学特征与 Vav1 蛋白阳性表达间的关系, Kaplan-Meier 法及 Cox 回归分析影响胃癌预后的相关因素。 结果 Vav1 mRNA 的表达在胃癌细胞株（HGC-27、SGC7901、MGC803）明显高于正常胃黏膜细胞株（GES-1）, 差异有统计学意义。 在胃癌组织标本中, Vav1 蛋白阳性表达与肿瘤直径和淋巴结转移有关（P < 0.05）。 预后生存分析中, 单因素分析显示肿瘤直径、分化程度、浸润深度、淋巴结转移和 Vav1 表达与胃癌预后有关（P < 0.05）。 Cox 回归显示肿瘤浸润深度深（HR=2.764, 95%CI 1.316～ 5.817, P=0.007）, 淋巴结转移分级高（HR=1.298, 95%CI 1.098～ 1.534, P=0.002）和 Vav1 阳性表达（HR= 2.577, 95%CI 1.066～ 3.946, P=0.006）是影响本组胃癌患者预后的危险因素。 结论 Vav1 在胃癌的侵袭和迁移中发挥着重要的作用, 且与胃癌预后密切相关。