肿瘤突变负荷在评价肝细胞癌预后中的价值和应用

目的：探讨肿瘤突变负荷（TMB）在评价肝细胞癌（HCC）预后中的价值，并建立应用模型。方法：纳入HCC患者107例，根据时间依赖性受试者工作特性（ROC）曲线及曲线下面积（AUC）确定TMB截断值。Cox回归分析TMB与总生存期（OS）的相关性。Kaplan-Meier法构建生存曲线，根据Cox回归分析结果构建列线图预后预测模型，通过校准曲线和决策曲线分析（DCA）验证列线图模型的可靠性。结果：根据时间依赖性ROC曲线和AUC确定TMB最佳截断值为3.3 mut/Mb。Kaplan-Meier结果显示，TMB高的患者OS较差[HR=1.68(1.09,2.59),P=0.020];Cox多因素回归分析结果显示，TMB是HCC的独立预后因子[HR=1.81(1.14,2.87),P=0.011]。将HCC独立预后因子（微血管侵犯、肿瘤转移、TMB）纳入列线图预后预测模型的构建，一致性指数为0.692(0.661,0.724)，校准曲线和DCA证实预后预测模型准确性良好。结论：TMB是HCC的独立预后因子，高TMB患者预后较差；以TMB等独立预后因子建立的预后预测模型准确性良好，有较高的临...     

基于生物信息学构建GOPC的皮肤黑素瘤预后模型

目的：基于生物信息学构建GOPC(golgi associated PDZ and coiled-coil motif containing)的皮肤黑素瘤（Skin cutaneous melanoma,SKCM）预后模型，进一步为SKCM的早期筛查发挥一定的指导意义。方法：提取癌症基因组图谱TCGA数据库中的SKCM数据和GTEx数据库对应的正常组织数据。利用ggplot2包绘制GOPC在两者之间的表达差异。采用基因本体（GO）数据库和京都基因与基因组百科全书（KEGG）数据库对GOPC基因进行进一步功能富集分析，探索该基因潜在的功能和通路。采用单因素/多因素Cox回归分析筛选与SKCM相关的影响因素，再通过rms包构建列线图建立SKCM患者预后模型，ROC曲线评价构建的模型预测SKCM患者预后的价值，最后通过survival包构建关于总生存期（Overall survival,OS）的生存曲线。结果：在1282例样本中正常组织样本有813例、皮肤黑素瘤组织样本有469例，发现GOPC在这两组之间有表达差异（P <0.001）。该模型预测SKCM患者总生存的ROC曲线下面积（A...     

基于铜死亡相关基因的肾透明细胞癌中预后模型的构建与应用

目的:研究铜死亡相关基因(cuproposis-related genes, CRGs)在肾透明细胞癌(clear cell renal cell carcinoma, ccRCC)中的表达情况，构建预后模型并探讨其临床意义。方法:从TCGA数据库中获得522例ccRCC患者的转录组数据和临床病理数据。将患者随机分为训练集(262例)和验证集1(260例),并将总体患者作为验证集2(522例)。在训练集中采用差异性分析确定肿瘤组织和癌旁组织差异性表达的CRGs,使用LASSO-Cox回归分析构建ccRCC的CRGs预后模型(OSCRG)。根据OSCRG将患者分为低危组和高危组，使用Kaplan-Meier生存分析研究OSCRG与总体生存期(overall survival, OS)的关系。使用单因素和多因素Cox回归分析构建包含OSCRG和临床病理参数的列线图预测OS,并在验证集中验证该列线图的准确性。最后使用KEGG和GO基因富集分析探索差异性表达的CRGs的生物学功能。结果:在训练集中有9个差异性表达的CRGs,并且均与OS相关。LASSO-Cox回归分析确定了3个CRGs并构建O...     

术前血清γ-谷氨酰转肽酶与血小板比值预测乙型肝炎病毒相关性肝细胞癌患者预后的预测模型构建

目的 探讨术前血清γ-谷氨酰转肽酶与血小板比值（gamma-glutamyl transpeptidase to platelet ratio,GPR）与行根治性切除术的乙型肝炎病毒相关性肝细胞癌（hepatocellular carcinoma,HCC）患者（简称“HCC患者”）预后的关系并建立列线图预测模型。方法 根据纳入和排除标准，回顾性收集2012年1月15日至2018年12月15日期间咸阳市中心医院肝胆外科收治的HCC患者的临床病理资料。应用受试者操作特征曲线确定GPR的最佳临界值，据此将患者分为低GPR组（GPR≤最佳临界值）和高GPR组（GPR>最佳临界值）；应用Kaplan-Meier法绘制生存曲线进行生存分析。应用单因素和多因素Cox比例风险回归（简称“Cox回归”）模型分析影响HCC患者总生存期的风险因素，根据筛选出的风险因素构建列线图预测模型，采用一致性指数和校准曲线评估它预测HCC患者3年和5年累积总生存率的效能。结果 共纳入213例患者，GPR的最佳临界值为0.906，其中低GPR组和高GPR组分别为114例和99例。Kaplan-Meier生存曲线分析...     

VAVs基因家族在肝癌中的表达及临床意义

目的 探究VAVs家族基因在肝细胞癌（HCC）患者中的表达水平及其在预后中的临床价值，并基于阳性作用基因建立肝癌的预后预测模型。方法 从肿瘤基因组图谱（TCGA）数据库中下载并提取342例HCC癌组织和50例癌旁组织中VAVs家族基因mRNA测序表达数据及其临床信息。比较TCGA中VAVs mRNA在HCC癌组织和癌旁组织中的表达差异，并比较温州医科大学附属第一医院20例HCC患者的癌组织及癌旁组织中VAVs蛋白的表达差异。根据VAVs mRNA表达量中位值分为高表达组（n=171）和低表达组（n=171），比较两组的临床病理特征差异，采用Kaplan-Meier法分析比较两组的生存差异。采用单因素和多因素Cox分析筛选HCC患者预后的危险因素。基于VAV2 mRNA构建HCC生存预后列线图模型，采用受试者工作特征（ROC）曲线及校正曲线来评估模型的预测准确性。结果 对TCGA数据库分析显示，VAV2 mRNA在HCC癌组织中的表达量高于癌旁组织（P<0.05）；本中心免疫组化结果表明，VAV2蛋白在HCC癌组织中的表达量高于癌旁组织（P<0.05）。TCGA数据库分析结果...     

肝癌铜死亡相关基因及其与预后、免疫浸润的关系分析

背景与目的 肝癌是消化道恶性肿瘤之一,发病率高、病死率高。铜死亡是一种铜依赖、新型的细胞死亡方式,继发于铜过载诱发的线粒体功能受损。铜死亡在多种肿瘤中扮演重要作用,但其与肝癌的关系尚不清楚。因此,本研究探讨铜死亡相关基因在肝癌中的表达特征,以及与肝癌预后及免疫浸润的关系。方法 从TCGA和GTEx数据库下载肝癌和正常肝脏组织转录组数据进行差异表达和突变分析。采用R语言“clusterProfiler”包进行GO和KEGG富集分析。采用LASSO、单因素和多因素回归分析筛选影响肝癌患者预后的基因并构建风险因子图。使用R包“rms”构建列线图。使用UALCAN数据库分析铜死亡相关基因与肝癌临床病理特征的关系并验证。使用Spearman相关性分析铜死亡相关基因与免疫细胞浸润和免疫检查点的相关性。采用TIMER2.0数据库分析铜死亡相关基因表达与肿瘤相关成纤维细胞（CAF）浸润的相关性,采用TISDB数据库分析CDKN2A和DLAT表达与髓源抑制性细胞（MDSC）浸润丰度的相关性。结果 与正常肝脏组织比较,9个铜死亡相关基因在肝癌中表达显著升高,CDKN2A突变频率最高。铜死亡相关基因主要参与蛋白质脂酰化、三羧酸循环、柠檬酸循环等生物过程。基于LASSO、单因素与多因素回归分析筛选出影响肝癌患者总体生存率（OS）的基因CDKN2A和DLAT,并以此构建风险因子图,时间依赖性受试者工作特征曲线显示其具有较好的预测能力。通过单因素和多因素回归分析筛选出CDKN2A、DLAT、T分期和肿瘤状态是影响肝癌患者OS的独立预后因素,基于上述因素构建了列线图,校正曲线显示该列线图预测和实际观察之间有很好的一致性。UALCAN数据库分析发现CDKN2A、DLAT与肝癌临床分期、肿瘤分级有关,且GEO数据库、HPA数据库及肝癌细胞中的验证结果与之一致。相关性分析显示,CDKN2A和DLAT表达与免疫细胞浸润和免疫检查点表达相关;TIMER2.0数据库分析显示,DLAT表达与CAF浸润明显正相关;TISDB数据库分析显示,CDKN2A和DLAT表达与MDSC浸润丰度无相关性。结论 铜死亡相关基因CDKN2A、DLAT可能是肝癌新的预后生物标志物和免疫治疗的新靶点。     

基于TCGA数据库构建肝细胞癌程序性细胞死亡的列线图预后预测模型

目的 筛选与预后有关的程序性细胞死亡(programmed cell death,PCD)相关的长链非编码RNA(long non-coding RNA,lncRNA)并以此构建肝细胞癌(hepatocellular carcinoma,HCC)的预后风险评估列线图。方法 将从癌症基因组图谱中选择的HCC患者按1∶1随机抽样分为训练集和验证集。采用Pearson相关性分析筛选PCD相关lncRNAs，再用单因素Cox比例风险回归(简称“Cox回归”)模型分析筛选与训练集中的总生存时间有关的PCD相关lncRNAs，然后进一步采用多因素Cox回归模型分析影响HCC患者的预后风险因素，并建立判断HCC患者的风险评分函数模型。根据训练集中HCC患者的中位风险评分将各集中的HCC患者分为高风险和低风险，然后采用Kaplan-Meier法绘制总生存曲线并采用log-rank检验比较高风险和低风险HCC患者总生存情况的差异；同时采用时间相关受试者操作特征曲线下面积(area under receiver operating characteristic curve,AUC)评估风险评分函数模型预测...     

PAQR4在肝细胞癌组织中的表达及其在细胞增殖、迁移和侵袭中的作用

目的 探讨孕激素和脂联素受体4（PAQR4）在肝细胞癌（HCC）中起到的调控作用。方法 （1）在TCGA数据库中调查HCC组织中PAQR4的表达,分析PAQR4与HCC患者预后的关系。（2）转染两种不同的PAQR4特异性siRNA致HCC细胞系Hep3B和Huh7中PAQR4的表达降低,通过细胞增殖、侵袭和迁移实验来分析PAQR4对HCC的影响。（3）通过TCGA获取数据并采用Pearson卡方检验评估PAQR4的表达与HCC组织中免疫细胞浸润的关系。结果 （1）HCC患者肝脏肿瘤组织中PAQR4表达水平高于癌旁组织（P＜0.05）,PAQR4高表达与HCC患者预后不良相关（P=0.0014）。（2）在Hep3B和Huh7细胞中敲低PAQR4可抑制细胞增殖、侵袭和迁移。（3）PAQR4在HCC组织中的高表达与多种免疫细胞浸润存在相关。结论 PAQR4与肝细胞癌具有相关性,敲低PAQR4会抑制HCC细胞的增殖、侵袭和迁移。     

术前淋巴细胞与C反应蛋白比值在预测胆囊癌术后复发中的应用及其临床价值

背景与目的：系统性炎症与大多数恶性肿瘤的发生发展密切相关,炎症相关评分的研究为改善癌症患者风险分层和患者预后提供了有效的预测信息。但目前尚缺乏关于炎症评分与胆囊癌（GBC）患者术后复发风险关系的研究。因此,本研究探讨术前淋巴细胞计数与C反应蛋白（CRP）比值（LCR）与胆囊癌患者术后复发的关系,并建立预测GBC术后复发风险的列线图模型。方法：回顾性分析中国人民解放军联勤保障部队第九〇〇医院2009年5月—2021年12月接受手术治疗的103例GBC患者的临床资料,绘制LCR预测术后复发的受试者工作特征曲线（ROC）,确定最佳临床临界值。根据临界值将GBC患者分为高LCR组和低LCR组,分析两组患者临床病理特征的差异及影响患者术后复发的危险因素,并根据危险因素的回归系数绘制相应的GBC患者术后复发的列线图预测模型,并通过校准曲线及一致性曲线进行验证。Kaplan-Meier法绘制生存曲线,并用Log-rank检验比较两组患者的总生存时间（OS）和无复发生存时间（RFS）的差异。结果：术前LCR预测GBC患者术后复发的ROC曲线下面积为0.681 (95%CI=0.560～0.802,P&...     

胃癌抗氧化相关lncRNAs预后风险评分模型的构建及生信分析

目的 ：探讨抗氧化相关长链非编码RNAs(lncRNAs)预后风险评分模型对胃癌患者预后的判断价值以及与免疫微环境的关系。方法：通过TCGA数据库下载胃癌转录组数据和临床信息。通过lnc RNAs和抗氧化基因的共表达分析得到抗氧化相关lncRNAs。使用单因素cox回归分析和lasso回归分析筛选并构建风险评分。采用Log-Rank检验比较两组间的生存差异。应用受试者工作特征（ROC）曲线评估预后风险模型对患者预后判断的特异性及敏感度。结合风险评分和临床参数构建列线图。TIMER2.0在线评估每个样本的免疫细胞浸润情况。TIDE网站在线分析每个样本对免疫治疗敏感性。结果：通过单因素cox回归分析和lasso回归分析构建了一个包括12个lncRNAs的风险评分。风险评分是患者预后的独立影响因素[HR=5.406(3.131～9.335),P<0.001]。风险评分与多种抑制性免疫细胞浸润呈正相关（M2型巨噬细胞、肿瘤相关成纤维细胞）。同时发现,高风险组存多种免疫检查点基因的异常表达,TIDE评分更高,提示高风险组对免疫治疗更敏感。结论：基于抗氧化相关lncRNAs风险评分和临床参数...     

肝细胞癌免疫相关lncRNA预后风险模型的建立与评估

背景与目的:肝细胞癌(HCC)目前是全球肿瘤死亡的主要原因之一,越来越多的证据表明,长非编码RNA (lncRNA)可以作为肿瘤预后的生物标志物.然而,lncRNA与HCC生存预后的关系仍未阐明.本研究筛选HCC预后免疫相关lncRNA,并构建预后风险模型.方法:从癌症基因组图谱(TCGA)中下载HCC转录组数据和临床...     

基于E2F靶点基因集和免疫亚型差异的肝细胞癌预后风险评分模型的建立

背景与目的：肝细胞癌（HCC）是肝癌中最常见的种类,HCC患者的预后生存情况较差,其有效的预后预测也面临巨大挑战。许多研究已证实E2F基因家族和免疫微环境相关的基因标志物是癌症的重要预后因素,因此,本研究利用TCGA数据库筛选E2F基因家族和免疫微环境相关的HCC基因标志物,建立新的HCC风险评分模型,并预测HCC潜在治疗靶点。方法：TCGA数据库中下载大型HCC (LIHC)队列（424例样本）。进行了基因集富集分析、基因集单样本富集分析和基因集单样本富集分析分数聚类后的基因表达差异分析,通过Lasso回归筛选标志基因并建模,根据模型计算患者得分并将患者分为高风险组和低风险组。使用受试者工作特征曲线（ROC）、Kaplan-Meier生存曲线、Cox回归分析等多种统计学方法以验证模型的可靠性。所有统计分析均使用R语言软件。最后在Cbioportal数据库查询风险模型的标志基因在TCGA-HCC样本中的基因变异情况,从String数据库中下载蛋白互作信息并用Cytoscape软件进行可视化分析。结果：确认了与HCC密切相关的E2F靶点基因组和免疫相关差异基因后,从中筛选到了与HCC患者...     

Development and validation of the prognostic value of the immune-related genes in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor, resulting a challenge of developing target therapeutics. Not long ago, immune checkpoint blockade regimens combine with tyrosin kinase inhibitors have evolved frontline options in metastatic RCC, which implies arrival of the era of tumor immunotherapy. Studies have demonstrated immune-related genes (IRGs) could characterize tumor milieu and related to patient survival. Nevertheless, the clinical significance of classifier depending on IRGs in ccRCC has not been well established.MethodsThe R package limma, univariate and LASSO cox regression analysis were used to screen the prognostic related IRGs from TCGA database. Multivariate cox regression was utilized to establish a risk prediction model for candidate genes. Quantitative real-time PCR was used to confirm the expression of candidates in clinical samples from our institution. CIBERSORT algorithm and correlation analysis were applied to explore tumor-infiltrating immune cells signature between different risk groups. A clinical nomogram was also developed to predict OS by using the rms R package based on the risk prediction model and other independent risk factors. The ICGC data was used for external validation of either gene risk model or nomogram.ResultsWe identified 382 differentially expressed immune related genes. Four unique prognostic IRGs (CRABP2, LTB4R, PTGER1 and TEK) were finally affirmed to associate with tumor survival independently and utilized to establish the risk score model. All candidates’ expression was successfully laboratory confirmed by q-PCR. CIBERSORT analysis implied patients in unfavorable-risk group with high CD8 T cell, regulatory T cell and NK cell infiltration, as well as high expression of PD-1, CTLA4, TNFRSF9, TIGIT and LAG3. A nomogram combined IRGs risk score with age, gender, TNM stage, Fuhrman grade, necrosis was further generated to predict of 3- and 5-year OS, which exhibited superior discriminative power (AUCs were 0.811 and 0.795).ConclusionsOur study established and validated a survival prognostic model system based on 4 unique immune related genes in ccRCC, which expands knowledge in tumor immune status and provide a potent prediction tool in future.     

联合多数据库构建肝细胞癌自噬基因预后模型

目的:构建自噬基因表达特征的肝细胞癌(hepatocellular carcinoma, HCC)患者预后的预测模型。方法:从癌症基因组图谱(The Cancer Genome Atlas, TCGA)、基因型-组织表达研究项目(The Genotype-Tissue Expression, GTEx)数据库中分别得到...     

Development and validation of a novel nomogram for predicting systemic inflammatory response syndrome’s occurrence in patients undertaking flexible ureteroscopy

BackgroundThe occurrence of systemic inflammatory response syndrome (SIRS) is an early alert for sepsis after flexible ureteroscopy (fURS). Once sepsis occurs, it often leads to severe or fatal consequences. We aimed to identify SIRS patients preoperatively by developing and validating a feasible prognostic nomogram model based on retrospective cohort analysis.MethodsA total of 311 patients who underwent fURS in Dongguan Kanghua Hospital (Dongguan, China) between 2016 and 2020 were included and randomly divided into a primary cohort (n=219) and validation cohort (n=92). Single factor regression analysis was used to identify the primary cohort’s meaningful characters between SIRS and non-SIRS groups. Factors of the primary cohort were then identified by least absolute shrinkage and selection operator (LASSO) regression analysis, and a nomogram was built to execute the subsequent analysis using these factors. Finally, we analyzed and drew the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve to validate the prognostic value of the nomogram in calibration and discrimination.ResultsReview of the single regression analysis of characters in the primary cohort showed gender, stone burden, diabetes, neutrophil (N), lymphocyte (L), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocytes ratio (LMR), urine-WBC, nitrite (Nit), urine culture, and surgery time as significant factors between the SIRS and non-SIRS groups (P<0.05). The LASSO regression analysis suggested NLR, PLR, and urine culture were substantial factors in predicting SIRS postoperatively, lambda.min and lambda.1se (standard error, SE) were 0.01491 and 0.0796. A nomogram built with the three factors showed good calibration and discrimination, with the Brier values 0.064 and 0.034 and the area under curve (AUC) values 0.897 (95% CI: 0.837–0.957) and 0.976 (95% CI: 0.947–1.000) in the primary and validation cohort, respectively. DCA demonstrated the nomogram was clinically useful, and the predict probability of SIRS’s occurrence was very close to the actual rate as the risk threshold increased by higher than 60% in clinical impact curve analysis.ConclusionsNLR, PLR, and urine culture were significantly related to the occurrence of SIRS’s after fURS. The nomogram with these three factors showed excellent calibration, discrimination, and clinical usefulness.     

Nomogram for predicting recurrence and metastasis of stage IA lung adenocarcinoma treated by videoassisted thoracoscopic lobectomy

ObjectiveThis study aimed to construct a nomogram to effectively predict recurrence and metastasis in patients with stage 1A lung adenocarcinoma after video-assisted thoracoscopic surgery (VATS) lobectomy.MethodsOur study included 337 patients. The 3-year recurrence-free survival rate and the 5-year recurrence-free survival (5-RFS) rate were analyzed. Multivariate Cox proportional hazards regression was conducted to identify independent risk factors. We established a nomogram and performed Harrell’s Concordance index, calibration plots, integrated discrimination improvement, and decision curve analyses to assess its discrimination and calibration.ResultsThe median follow-up time was 45 months. In a multivariate analysis, tumor diameter, pathological subtype, preoperative carcinoembryonic antigen level, and preoperative CYFRA21-1 level were independent prognostic factors for RFS (P < 0.05). These risk factors were used to construct a nomogram to predict postoperative recurrence and metastasis in these patients. Internal verification was performed using the bootstrap method. The C-index was 0.946 (95% confidence interval: 0.923–0.970), indicating that the model had a good predictive performance. Using the nomogram and X-tile software, the patients were divided into two groups: the high-risk (5-RFS rate, 0.10–0.90) and low-risk groups (5-RFS rate, 0.90–0.99); the difference in the RFS rate between the groups was significant (χ2 = 86.705, P < 0.001).ConclusionsOur nomogram had a better predictive ability for recurrence and metastasis in patients with stage 1A lung adenocarcinoma after VATS lobectomy resection than the Tumor–Node–Metastasis staging system and other predictive models. This nomogram can help provide individualized treatment strategies and follow-up times.     

KIF4A在肝细胞癌中的表达及预后价值分析

背景与目的:肝细胞癌(HCC)是原发性肝癌最常见的病理类型,其起病隐匿,预后较差,位居癌症相关死亡原因第3位.KIF4A在多种恶性肿瘤中呈高表达且与不良预后密切相关,然而其在HCC中的作用及机制尚不清楚.因此,本研究分析KIF4A基因在HCC中的表达情况及预后价值,并探讨相关的分子机制.方法:从癌症基因组图谱(TCGA...     

Construction of a predictive model for clinical survival in male patients with non-metastatic rectal adenocarcinoma

BackgroundNo clinical prediction model is available for non-metastatic rectal adenocarcinoma in males. Based on demographic and clinicopathological characteristics, we constructed a survival prediction model for the study population.MethodsAt a ratio of 7:3, 3450 eligible patients were divided into training and validation sets. Optimal cutoff values were calculated using X-tile software. Cox proportional hazards regression was used to find prognostic factors for cancer-specific survival (CSS) and overall survival (OS). Corresponding nomogram prognostic models were also constructed based on predictors.The validity, discriminative ability, predictability, and clinical usefulness of the model were analyzed and assessed.ResultsWe identified predictors of survival in the target population and successfully constructed nomograms. In the nomogram prediction model for OS and CSS, the C-index was 0.724 and 0.735, respectively, for the training group and 0.754 and 0.760, respectively, for the validation group. In the validation group, the area under the curve (AUC) of the receiver operating characteristic curve for OS and CSS nomograms was 0.768 and 0.769, respectively, for the 3-year survival rate and 0.755 and 0.747, respectively, for the 5-year survival rate. Kaplan–Meier Survival Curves showed excellent risk discrimination performance of the nomogram (P < 0.05) Calibration curves, time-dependent AUC and decision curve analysis showed that the prediction model constructed in this study had excellent clinical prediction and decision ability and performed better than the TNM staging system.ConclusionOur nomogram is helpful to evaluate the prognosis of non-metastatic male patients with rectal adenocarcinoma and has guiding significance for clinical treatment.     

BRCA1/2基因突变对乳腺癌保乳术后局部复发的影响及相关预后模型的构建

背景与目的 保乳手术现已成为乳腺癌的标准手术方式之一,保乳手术能够保留患者的乳房外形,极大地改善患者术后的心理状态和生活质量。BRCA1/2基因是与乳腺癌密切相关的易感基因,BRCA1/2基因突变对保乳术后乳腺癌患者局部复发的影响目前尚有争议。因此,本研究分析BRCA1/2基因突变与乳腺癌保乳术后局部复发的关系,并构建相关预测模型,预测保乳术后乳腺癌患者的无局部复发生存（LRFS）率,为乳腺癌患者保乳手术适应证的选择提供可靠的依据。方法 回顾性分析2014年6月—2016年6月于中国人民解放军空军军医大学第一附属医院进行保乳手术的189例乳腺癌患者临床资料,并比较不同临床病理特征下患者BRCA1/2基因突变的差异,通过单因素及多因素Cox等比例回归模型分析BRCA1/2基因突变及其它临床病理因素对乳腺癌患者保乳术后局部复发的影响,并构建列线图来预测患者的LRFS率。通过一致性指数（C-index）、受试者工作特征（ROC）曲线及曲线下面积（AUC）对模型进行内部验证,通过校准曲线评估模型的准确性,并通过临床决策曲线分析（DCA）评价模型的临床获益和应用价值。结果 BRCA1/2基因突变组和未突变组的年龄和分子分型进行差异有统计学意义（均P<0.05）。单因素Cox等比例回归模型分析结果显示,BRCA1/2突变、肿瘤分级、肿瘤大小、N分期及分子分型是保乳术后乳腺癌患者LRFS率的影响因素（均P<0.1）。多因素Cox等比例回归模型分析结果显示,BRCA1/2基因突变、肿瘤大小、N分期及分子分型是保乳术后乳腺癌患者局部复发的独立影响因素（P<0.05）。将这些因素纳入并建立LRFS率的列线图预测模型。模型的C-index为0.86,内部验证C-index为0.81。ROC曲线分析结果显示,模型的3、5年LRFS率预测的AUC分别为0.89、0.85;校准曲线显示列线图预测的LRFS率与实际LRFS率接近;DCA分析显示模型的临床获益及应用价值较高。结论 BRCA1/2基因突变与保乳术后乳腺癌患者的局部复发相关,基于BRCA1/2基因突变列线图模型能够准确地预测保乳术后乳腺癌患者的LRFS率,并为乳腺癌患者手术方式的选择提供有效的科学依据。