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Development of Prediction Model for 5-year Survival of Colorectal Cancer. 开发结直肠癌 5 年生存率预测模型
IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-04 eCollection Date: 2024-01-01 DOI: 10.1177/11769351241275889
Raoof Nopour

Objectives: This study aims to introduce a prediction model based on a machine learning approach as an efficient solution for prediction purposes to better prognosis and increase CRC survival.

Methods: In the current retrospective study, we used the data of 1062 CRC cases to analyse and establish a prediction model for the 5-year CRC survival. The machine learning algorithms were used to develop prediction models, including random Forest, XG-Boost, bagging, logistic regression, support vector machine, artificial neural network, decision tree, and K-nearest neighbours.

Results: The current study revealed that the XG-Boost with AU-ROC of 0.906 and 0.813 for internal and external conditions gave us better insight into predictability and generalizability than other algorithms.

Conclusion: XG-Boost can be utilised as a knowledge source for implementing intelligent systems as an assistive tool for clinical decision-making in healthcare settings to improve prognosis and increase CRC survival through various clinical solutions that doctors can achieve.

研究目的本研究旨在引入一种基于机器学习方法的预测模型,作为一种有效的预测解决方案,以改善预后并提高 CRC 的存活率:在本次回顾性研究中,我们使用了 1062 例 CRC 病例的数据,分析并建立了 CRC 5 年生存率预测模型。方法:在本次回顾性研究中,我们利用 1062 例 CRC 病例数据分析并建立了 CRC 5 年生存率预测模型,其中包括随机森林(random Forest)、XG-Boost、bagging、逻辑回归、支持向量机、人工神经网络、决策树和 K-nearest neighbours 等机器学习算法:目前的研究显示,XG-Boost 在内部和外部条件下的 AU-ROC 分别为 0.906 和 0.813,与其他算法相比,XG-Boost 能更好地洞察可预测性和可推广性:XG-Boost可以作为一种知识源,用于实施智能系统,作为医疗机构临床决策的辅助工具,通过医生可以实现的各种临床解决方案,改善预后,提高CRC的存活率。
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引用次数: 0
Comprehensive Analysis of CCAAT/Enhancer Binding Protein Family in Ovarian Cancer. 全面分析卵巢癌中的 CCAAT/突变体结合蛋白家族
IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-04 eCollection Date: 2024-01-01 DOI: 10.1177/11769351241275877
Jiahong Tan, Daoqi Wang, Wei Dong, Lei Nian, Fen Zhang, Han Zhao, Jie Zhang, Yun Feng

Background: Ovarian cancer has brought serious threats to female health. CCAAT/enhancer binding proteins (C/EBPs) are key transcription factors involved in ovarian cancer. Therefore, comprehensive profiling C/EBPs in ovarian cancer is needed.

Methods: A comprehensive analysis concerning C/EBPs in ovarian cancer was performed. Firstly, detailed expression of C/EBP family members was integrally retrieved and then confirmed using immunohistochemistry. The regulatory effects and transcription regulatory functions of C/EBPs were studied by using regulatory network analysis and enrichment analysis. Using survival analysis, receiver operating characteristic curve analysis, and target-disease association analysis, the predictive prognostic value of C/EBPs on survival and drug responsiveness was systematically evaluated. The effects of C/EBPs on tumor immune infiltration were also assessed.

Results: Ovarian cancer tissues expressed increased CEBPA, CEBPB, and CEBPG but decreased CEBPD when compared with normal control tissues. The overall alteration frequency of C/EBPs in ovarian cancer was approaching 30%. C/EBP family members formed a reciprocal regulatory network involving carcinogenesis and had pivotal transcription regulatory functions. C/EBPs could affect survival of ovarian cancer and correlated with poor survival outcomes (OS: HR = 1.40, P = .0053 and PFS: HR = 1.41, P = .0036). Besides, expression of CEBPA, CEBPB, CEBPD, and CEBPE could predict platinum and taxane responsiveness of ovarian cancer. C/EBPs also affected immune infiltration of ovarian cancer.

Conclusions: C/EBPs were closely involved in ovarian cancer and exerted multiple biological functions. C/EBPs could be exploited as prognostic and predictive biomarkers in ovarian cancer.

背景介绍卵巢癌严重威胁女性健康。CCAAT/增强子结合蛋白(C/EBPs)是参与卵巢癌的关键转录因子。因此,需要对卵巢癌中的 C/EBPs 进行全面分析:方法:对卵巢癌中的 C/EBPs 进行了全面分析。方法:对卵巢癌中的 C/EBPs 进行了全面分析。首先,综合检索了 C/EBP 家族成员的详细表达情况,然后用免疫组化法进行了确认。利用调控网络分析和富集分析研究了 C/EBPs 的调控效应和转录调控功能。通过生存分析、接收者操作特征曲线分析和靶点-疾病关联分析,系统评估了C/EBPs对生存和药物反应性的预测预后价值。研究还评估了C/EBPs对肿瘤免疫浸润的影响:结果:与正常对照组织相比,卵巢癌组织表达的 CEBPA、CEBPB 和 CEBPG 增加,但 CEBPD 减少。卵巢癌中 C/EBPs 的总体改变频率接近 30%。C/EBP家族成员形成了一个涉及癌变的相互调控网络,具有关键的转录调控功能。C/EBPs可影响卵巢癌的生存,并与不良生存结果相关(OS:HR = 1.40,P = .0053;PFS:HR = 1.41,P = .0036)。此外,CEBPA、CEBPB、CEBPD 和 CEBPE 的表达可预测卵巢癌对铂类和紫杉类药物的反应性。C/EBPs还影响卵巢癌的免疫浸润:结论:C/EBPs与卵巢癌密切相关,并具有多种生物学功能。结论:C/EBPs与卵巢癌密切相关,具有多种生物学功能,可作为卵巢癌的预后和预测生物标志物。
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引用次数: 0
Nine Human Leukocyte Antigen (HLA) Class I Alleles are Omnipotent Against 11 Antigens Expressed in Melanoma Tumors. 九种人类白细胞抗原 (HLA) I 类等位基因对黑色素瘤肿瘤中表达的 11 种抗原具有全能性。
IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-27 eCollection Date: 2024-01-01 DOI: 10.1177/11769351241274160
Apostolos P Georgopoulos, Lisa M James, Matthew Sanders

Objective: Host immunogenetics (Human Leukocyte Antigen, HLA) play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes hinge on the successful binding of epitopes of melanoma antigens to HLA Class I molecules for an effective engagement of cytotoxic CD8+ lymphocytes and subsequent elimination of the cancerous cell. This study evaluated the binding affinity and immunogenicity of HLA Class I to melanoma tumor antigens to identify alleles best suited to facilitate elimination of melanoma antigens.

Methods: In this study, we used freely available software tools to determine in silico the binding affinity and immunogenicity of 2462 reported HLA Class I alleles to all linear nonamer epitopes of 11 known antigens expressed in melanoma tumors (TRP2, S100, Tyrosinase, TRP1, PMEL(17), MAGE1, MAGE4, CTA, BAGE, GAGE/SSX2, Melan).

Results: We identified the following 9 HLA Class I alleles with very high immunogenicity and binding affinity against all 11 melanoma antigens: A*02:14, B*07:10, B*35:10, B*40:10, B*40:12, B*44:10, C*07:11, and C*07:13, and C*07:14.

Conclusion: These 9 HLA alleles possess the potential to aid in the elimination of melanoma both by themselves and by enhancing the beneficial effect of immune checkpoint inhibitors.

目的:宿主免疫遗传学(人类白细胞抗原,HLA)在人类对黑色素瘤的免疫反应中起着至关重要的作用,影响着黑色素瘤的发病率和免疫疗法的效果。疗效取决于黑色素瘤抗原表位与 HLA I 类分子的成功结合,从而使细胞毒性 CD8+ 淋巴细胞有效参与并随后消灭癌细胞。本研究评估了HLA I类分子与黑色素瘤肿瘤抗原的结合亲和力和免疫原性,以确定最适合促进消除黑色素瘤抗原的等位基因:在这项研究中,我们使用可免费获得的软件工具,对已报道的2462个HLA I类等位基因与黑色素瘤肿瘤中表达的11种已知抗原(TRP2、S100、酪氨酸酶、TRP1、PMEL(17)、MAGE1、MAGE4、CTA、BAGE、GAGE/SSX2、Melan)的所有线性非等位基因表位的结合亲和力和免疫原性进行了硅学测定:我们确定了以下 9 个 HLA I 类等位基因,它们对所有 11 种黑色素瘤抗原具有极高的免疫原性和结合亲和力:A*02:14、B*07:10、B*35:10、B*40:10、B*40:12、B*44:10、C*07:11、C*07:13 和 C*07:14:这 9 个 HLA 等位基因本身就有可能帮助消除黑色素瘤,而且还能增强免疫检查点抑制剂的有益效果。
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引用次数: 0
Identification of Copper Homeostasis-Related Gene Signature for Predicting Prognosis in Patients with Epithelial Ovarian Cancer. 鉴定铜平衡相关基因特征以预测上皮性卵巢癌患者的预后
IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-13 eCollection Date: 2024-01-01 DOI: 10.1177/11769351241272400
Ping Yan, Yueqin Tian, Xiaojing Li, Shuangmei Li, Haidong Wu, Tong Wang

Objectives: This research aims to establish a copper homeostasis-related gene signature for predicting the prognosis of epithelial ovarian cancer and to investigate its underlying mechanisms.

Methods: We mainly constructed the copper homeostasis-related gene signature by LASSO regression analysis. Then multiple methods were used to evaluate the independent predictive ability of the model and explored the mechanisms.

Results: The 15-copper homeostasis-related gene (15-CHRG) signature was successfully established. Utilizing an optimal cut-off value of 0.35, we divided the training dataset into high-risk and low-risk subgroups. Kaplan-Meier analysis revealed that survival times for the high-risk subgroup were significantly shorter than those in the low-risk group (P < .05). Additionally, the Area Under the Curve (AUC) of the 15-CHRG signature achieved 0.822 at 1 year, 0.762 at 3 years, and 0.696 at 5 years in the training set. COX regression analysis confirmed the 15-CHRG signature as both accurate and independent. Gene set enrichment (GSEA), Kyoto Encyclopedia of Gene and Genome (KEGG) and Gene Ontology (GO) analysis showed that there were significant differences in apoptosis, p53 pathway, protein synthesis, hydrolase and transport-related pathways between high-risk group and low-risk group. In tumor immune cell (TIC) analysis, the increased expression of resting mast cells was positively correlated with the risk score.

Conclusion: Consequently, the 15-CHRG signature shows significant potential as a method for accurately predicting clinical outcomes and treatment responses in patients with epithelial ovarian cancer.

研究目的本研究旨在建立预测上皮性卵巢癌预后的铜稳态相关基因特征,并探讨其潜在机制:方法:主要通过 LASSO 回归分析构建铜稳态相关基因特征。方法:我们主要通过 LASSO 回归分析法构建了铜稳态相关基因特征,然后采用多种方法评估了模型的独立预测能力并探讨了其机制:结果:成功建立了15个铜稳态相关基因(15-CHRG)特征。利用0.35的最佳临界值,我们将训练数据集分为高危和低危亚组。Kaplan-Meier 分析显示,高风险亚组的生存时间明显短于低风险组(P 结论:高风险亚组的生存时间明显短于低风险组):因此,15-CHRG 特征在准确预测上皮性卵巢癌患者的临床结局和治疗反应方面显示出巨大的潜力。
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引用次数: 0
Unveiling the Significance of NCAP Family Genes in Adrenocortical Carcinoma and Adenoma Pathogenesis: A Molecular Bioinformatics Exploration. 揭示 NCAP 家族基因在肾上腺皮质癌和腺瘤发病机制中的意义:分子生物信息学探索。
IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-23 eCollection Date: 2024-01-01 DOI: 10.1177/11769351241262211
Mahshid Arastonejad, Daniyal Arab, Somayeh Fatemi, Pezhman Golshanrad

Objectives: Adrenocortical carcinoma (ACC), a rare and aggressive adrenal cortex cancer, poses significant challenges due to high mortality, poor prognosis, and early post-surgery recurrence. Variability in survival across ACC stages emphasizes the need to uncover its molecular underpinnings. Adrenocortical adenoma, a benign tumor, adds to diagnostic challenges, highlighting the necessity for molecular insights. The Non-SMC Associated Condensin Complex (NCAP) gene family, recognized for roles in chromosomal structure and cell cycle control. This study focuses on evaluating NCAP gene functions and importance in ACC through gene expression profiling to identify diagnostic and therapeutic targets.

Methods: Microarray datasets from ACC patients, obtained from the Gene Expression Omnibus database, were normalized to eliminate batch effects. Differential expression analysis of NCAP family genes, facilitated by the GEPIA2 database, included survival and pathological stage evaluations. A Protein-Protein Interaction network was constructed using GeneMANIA, and additional insights were gained through Gene Ontology enrichment analysis, correlation analysis, and ROC curve analysis.

Results: ACC samples exhibited elevated levels of NCAPG, NCAPG2, and NCAPH compared to normal and adenoma samples. Increased expression of these genes correlated with poor overall survival, particularly in advanced disease stages. The Protein-Protein Interaction network highlighted interactions with related proteins, and Gene Ontology enrichment analysis demonstrated their involvement in chromosomal structure and control. Differentially expressed NCAP genes showed positive associations, and ROC curve analysis indicated their high discriminatory power in identifying ACC from adenoma and normal samples.

Conclusion: The study emphasizes the potential importance of NCAPG, NCAPG2, and NCAPH in ACC, suggesting roles in tumor aggressiveness and diagnostic relevance. These genes could serve as therapeutic targets and markers for ACC, but further exploration into their molecular activities and validation studies is imperative to fully harness their diagnostic and therapeutic potential, advancing precision medicine approaches against this rare but lethal malignancy.

目的:肾上腺皮质癌(ACC)是一种罕见的侵袭性肾上腺皮质癌,由于死亡率高、预后差和术后早期复发,给研究带来了巨大挑战。肾上腺皮质癌各期生存率的差异强调了揭示其分子基础的必要性。肾上腺皮质腺瘤是一种良性肿瘤,它给诊断带来了更多挑战,凸显了分子研究的必要性。非 SMC 相关凝集素复合物(NCAP)基因家族在染色体结构和细胞周期控制方面的作用已得到公认。本研究的重点是通过基因表达谱分析评估NCAP基因在ACC中的功能和重要性,以确定诊断和治疗靶点:方法:从基因表达总库(Gene Expression Omnibus)数据库中获取ACC患者的微阵列数据集,并对其进行归一化处理以消除批次效应。通过GEPIA2数据库对NCAP家族基因进行差异表达分析,包括生存期和病理分期评估。利用GeneMANIA构建了蛋白质-蛋白质相互作用网络,并通过基因本体富集分析、相关性分析和ROC曲线分析获得了更多的见解:与正常样本和腺瘤样本相比,ACC样本中的NCAPG、NCAPG2和NCAPH水平升高。这些基因表达的增加与总生存率低有关,尤其是在疾病晚期。蛋白-蛋白相互作用网络强调了与相关蛋白的相互作用,基因本体富集分析表明这些基因参与了染色体结构和控制。差异表达的NCAP基因显示了正相关性,ROC曲线分析表明它们在从腺瘤和正常样本中鉴别ACC方面具有很高的鉴别力:该研究强调了 NCAPG、NCAPG2 和 NCAPH 在 ACC 中的潜在重要性,表明它们在肿瘤侵袭性和诊断相关性中的作用。这些基因可作为 ACC 的治疗靶点和标记物,但要充分利用它们的诊断和治疗潜力,推进针对这种罕见但致命的恶性肿瘤的精准医疗方法,进一步探索它们的分子活动和验证研究势在必行。
{"title":"Unveiling the Significance of NCAP Family Genes in Adrenocortical Carcinoma and Adenoma Pathogenesis: A Molecular Bioinformatics Exploration.","authors":"Mahshid Arastonejad, Daniyal Arab, Somayeh Fatemi, Pezhman Golshanrad","doi":"10.1177/11769351241262211","DOIUrl":"10.1177/11769351241262211","url":null,"abstract":"<p><strong>Objectives: </strong>Adrenocortical carcinoma (ACC), a rare and aggressive adrenal cortex cancer, poses significant challenges due to high mortality, poor prognosis, and early post-surgery recurrence. Variability in survival across ACC stages emphasizes the need to uncover its molecular underpinnings. Adrenocortical adenoma, a benign tumor, adds to diagnostic challenges, highlighting the necessity for molecular insights. The Non-SMC Associated Condensin Complex (NCAP) gene family, recognized for roles in chromosomal structure and cell cycle control. This study focuses on evaluating NCAP gene functions and importance in ACC through gene expression profiling to identify diagnostic and therapeutic targets.</p><p><strong>Methods: </strong>Microarray datasets from ACC patients, obtained from the Gene Expression Omnibus database, were normalized to eliminate batch effects. Differential expression analysis of NCAP family genes, facilitated by the GEPIA2 database, included survival and pathological stage evaluations. A Protein-Protein Interaction network was constructed using GeneMANIA, and additional insights were gained through Gene Ontology enrichment analysis, correlation analysis, and ROC curve analysis.</p><p><strong>Results: </strong>ACC samples exhibited elevated levels of NCAPG, NCAPG2, and NCAPH compared to normal and adenoma samples. Increased expression of these genes correlated with poor overall survival, particularly in advanced disease stages. The Protein-Protein Interaction network highlighted interactions with related proteins, and Gene Ontology enrichment analysis demonstrated their involvement in chromosomal structure and control. Differentially expressed NCAP genes showed positive associations, and ROC curve analysis indicated their high discriminatory power in identifying ACC from adenoma and normal samples.</p><p><strong>Conclusion: </strong>The study emphasizes the potential importance of NCAPG, NCAPG2, and NCAPH in ACC, suggesting roles in tumor aggressiveness and diagnostic relevance. These genes could serve as therapeutic targets and markers for ACC, but further exploration into their molecular activities and validation studies is imperative to fully harness their diagnostic and therapeutic potential, advancing precision medicine approaches against this rare but lethal malignancy.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11265250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ExGenet, Integrating Design of Experiments and Response Surface Methodology for Cancer Gene Detection in Gene Regulatory Networks. ExGenet,整合实验设计和响应面方法,用于基因调控网络中的癌症基因检测。
IF 2 Q3 Medicine Pub Date : 2024-06-06 eCollection Date: 2024-01-01 DOI: 10.1177/11769351241255645
Mahboube Ayoubi, Babak Teimourpour, Alireza Hassanzadeh

Objective: Network analysis techniques often require tuning hyperparameters for optimal performance. For instance, the independent cascade model necessitates determining the probability of diffusion. Despite its importance, a consensus on effective parameter adjustment remains elusive.

Methods: In this study, we propose a novel approach utilizing experimental design methodologies, specifically 2-Factorial Analysis for Screening, and Response Surface Methodology (RSM) for parameter adjustment. We apply this methodology to the task of detecting cancer driver genes in colorectal cancer.

Result: Through experimental validation of colorectal cancer data, we demonstrate the effectiveness of our proposed methodology. Compared with existing methods, our approach offers several advantages, including reduced computational overhead, systematic parameter selection grounded in statistical theory, and improved performance in detecting cancer driver genes.

Conclusion: This study presents a significant advancement in the field of network analysis by providing a practical and systematic approach to hyperparameter tuning. By optimizing parameter settings, our methodology offers promising implications for critical biomedical applications such as cancer driver gene detection.

目的:网络分析技术通常需要调整超参数以获得最佳性能。例如,独立级联模型需要确定扩散概率。尽管超参数非常重要,但人们仍未就有效的参数调整达成共识:在本研究中,我们提出了一种利用实验设计方法的新方法,特别是用于筛选的 2 因子分析法和用于参数调整的响应面方法(RSM)。我们将该方法应用于检测结直肠癌中的癌症驱动基因:通过对结直肠癌数据的实验验证,我们证明了所提方法的有效性。与现有方法相比,我们的方法具有多项优势,包括减少了计算开销、基于统计理论的系统化参数选择以及提高了检测癌症驱动基因的性能:本研究提供了一种实用、系统的超参数调整方法,在网络分析领域取得了重大进展。通过优化参数设置,我们的方法为癌症驱动基因检测等关键生物医学应用提供了前景广阔的影响。
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引用次数: 0
Alternative Polyadenylation Regulatory Factors Signature for Survival Prediction in Kidney Renal Cell Carcinoma. 用于肾脏肾细胞癌生存预测的替代多腺苷酸化调控因子图谱
IF 2 Q3 Medicine Pub Date : 2024-04-12 eCollection Date: 2024-01-01 DOI: 10.1177/11769351231180789
Xiaoyu Wang, Yao Lin, Zheng Li, Yueqi Li, Mingcong Chen

Background: Alternative polyadenylation (APA) plays a vital regulatory role in various diseases. It is widely accepted that APA is regulated by APA regulatory factors.

Objective: Whether APA regulatory factors affect the prognosis of renal cell carcinoma remains unclear, and this is the main topic of this study.

Methods: We downloaded the transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database. We used the Lasso regression system to construct an APA model for analyzing the relationship between common APA regulatory factors and renal cell carcinoma. We also validated our APA model using independent GEO datasets (GSE29609, GSE76207).

Results: It was found that the expression levels of 5 APA regulatory factors (CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4) were significantly associated with tumor gene mutation burden (TMB) score in renal clear cell carcinoma, and the risk score constructed using the expression level of 5 key APA regulatory factors could be used to predict the outcome of renal clear cell carcinoma. The TMB score is associated with the remodeling of the immune microenvironment.

Conclusions: By identifying key APA regulatory factors in renal cell carcinoma and constructing risk scores for key APA regulatory factors, we showed that key APA regulators affect prognosis of renal clear cell carcinoma patients. In addition, the risk score level is associated with TMB, indicating that APA may affect the efficacy of immunotherapy through immune microenvironment-related genes. This helps us better understand the mRNA processing mechanism of renal clear cell carcinoma.

背景:替代多腺苷酸化(APA)在各种疾病中发挥着重要的调控作用。人们普遍认为,APA受APA调节因子的调控:APA调节因子是否影响肾细胞癌的预后仍不清楚,这是本研究的主要课题:我们从癌症基因组图谱(TCGA)数据库中下载了转录组和临床数据。方法:我们从癌症基因组图谱(TCGA)数据库中下载了转录组和临床数据,并使用 Lasso 回归系统构建了一个 APA 模型,用于分析常见 APA 调控因子与肾细胞癌之间的关系。我们还利用独立的 GEO 数据集(GSE29609、GSE76207)验证了我们的 APA 模型:结果发现,5个APA调控因子(CPSF1、CPSF2、CSTF2、PABPC1和PABPC4)的表达水平与肾透明细胞癌的肿瘤基因突变负荷(TMB)评分显著相关,利用5个关键APA调控因子的表达水平构建的风险评分可用于预测肾透明细胞癌的预后。TMB评分与免疫微环境的重塑有关:通过识别肾细胞癌的关键APA调控因子并构建关键APA调控因子的风险评分,我们发现关键APA调控因子会影响肾透明细胞癌患者的预后。此外,风险评分水平与TMB相关,表明APA可能通过免疫微环境相关基因影响免疫治疗的疗效。这有助于我们更好地理解肾透明细胞癌的mRNA处理机制。
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引用次数: 0
A Paradigm Shift in Non-Small-Cell Lung Cancer (NSCLC) Diagnostics: From Single Gene Tests to Comprehensive Genomic Profiling. 非小细胞肺癌(NSCLC)诊断范式的转变:从单一基因测试到综合基因组分析。
IF 2 Q3 Medicine Pub Date : 2024-04-05 eCollection Date: 2024-01-01 DOI: 10.1177/11769351241243243
Ushna Zameer, Wajiha Shaikh, Abdul Moiz Khan

Lung cancer imposes a burden on the health care system worldwide affecting 2 million people and causing 1.8 million deaths in 2021.More than 85% of all lung cancer cases are reported under Non-small-cell lung cancer (NSCLC). It is critical to discover gene alterations to treat non-small cell lung cancer successfully. The CAP/IASLC/AMP recommendations supported use of polymerase chain reaction (PCR) and fluorescent in situ hybridization (FISH) EGFR (epidermal growth factor receptor) mutations and ALK (Anaplastic lymphoma kinase) rearrangements, respectively. A study presented in the annual meeting of the American Society of Clinical Oncology (ASCO) in Chicago emphasized the need for comprehensive genomic profiling (CGP) before single gene tests (SGTs) since it demonstrated that SGT can result in the depletion of precious biopsy samples. As a result, the efficacy of thorough genetic Profiling (CGP) is reduced, preventing patients from receiving valuable genetic information about their tumors.

肺癌给全球医疗系统带来了沉重负担,2021 年全球将有 200 万人罹患肺癌,180 万人因此死亡。发现基因改变对成功治疗非小细胞肺癌至关重要。CAP/IASLC/AMP建议分别支持使用聚合酶链反应(PCR)和荧光原位杂交(FISH)检测表皮生长因子受体(EGFR)突变和ALK(无性淋巴瘤激酶)重排。在芝加哥举行的美国临床肿瘤学会(ASCO)年会上发表的一项研究强调,在进行单基因检测(SGTs)之前,有必要先进行全面基因组分析(CGP),因为该研究表明,单基因检测会导致珍贵的活检样本损耗殆尽。因此,全面基因组分析(CGP)的效果会降低,使患者无法获得有关其肿瘤的宝贵基因信息。
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引用次数: 0
An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing. 基于机器学习和基于联合核的监督哈希算法的智能搜索与检索系统(IRIS)以及用于决策支持的临床与研究资料库。
IF 2 Q3 Medicine Pub Date : 2024-02-04 eCollection Date: 2024-01-01 DOI: 10.1177/11769351231223806
David J Foran, Wenjin Chen, Tahsin Kurc, Rajarshi Gupta, Jakub Roman Kaczmarzyk, Luke Austin Torre-Healy, Erich Bremer, Samuel Ajjarapu, Nhan Do, Gerald Harris, Antoinette Stroup, Eric Durbin, Joel H Saltz

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.

在肿瘤学的广泛研究和临床活动中,大规模、多地点合作正变得不可或缺。为了促进下一代癌症生物学、精准肿瘤学和群体科学的发展,有必要开发和实施数据管理和分析工具,使研究人员能够可靠、客观地检测、描述和记录从良性状态向癌症状态转变过程中以及整个疾病进展过程中发生的表型和基因组变化。为了促进这些工作,信息学界有责任建立工作流程和架构,以自动汇总和组织范围和数量不断扩大的临床数据类型和模式,从新的分子和实验室测试到复杂的诊断成像研究。为了应对这些挑战,全国领先的医疗保健中心正在进行大量投资,以建立全企业范围的数据仓库。然而,许多数据仓库的一个重大局限是,它们在设计上只能支持字母数字信息。与这些传统设计不同,我们开发的系统支持自动收集和挖掘多模态数据,包括基因组学、数字病理学和放射学图像。在本文中,我们的团队介绍了多模态临床与研究数据仓库(CRDW)的设计、开发和实施,该数据仓库与一整套计算和机器学习工具紧密集成,可为肿瘤环境的潜在特征提供可操作的洞察力,而这些特征是标准方法和工具无法揭示的。该系统具有灵活的提取、转换和加载(ETL)接口,可根据特定部署地点使用的特定电子病历和其他数据源,对来自不同临床和研究来源的数据进行聚合。
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引用次数: 0
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries. 为转移性乳腺癌设计一个深度学习驱动的资源高效诊断系统:减少临床诊断的长时间延误,提高发展中国家患者的生存率。
IF 2 Q3 Medicine Pub Date : 2023-11-26 eCollection Date: 2023-01-01 DOI: 10.1177/11769351231214446
William Gao, Dayong Wang, Yi Huang

Breast cancer is one of the leading causes of cancer mortality. Breast cancer patients in developing countries, especially sub-Saharan Africa, South Asia, and South America, suffer from the highest mortality rate in the world. One crucial factor contributing to the global disparity in mortality rate is long delay of diagnosis due to a severe shortage of trained pathologists, which consequently has led to a large proportion of late-stage presentation at diagnosis. To tackle this critical healthcare disparity, we have developed a deep learning-based diagnosis system for metastatic breast cancer that can achieve high diagnostic accuracy as well as computational efficiency and mobile readiness suitable for an under-resourced environment. We evaluated 4 Convolutional Neural Network (CNN) architectures: MobileNetV2, VGG16, ResNet50 and ResNet101. The MobileNetV2-based diagnostic model outperformed the more complex VGG16, ResNet50 and ResNet101 models in diagnostic accuracy, model generalization, and model training efficiency. The ROC AUC of MobilenetV2 (0.933, 95% CI: 0.930, 0.936) was higher than VGG16 (0.911, 95% CI: 0.908, 0.915), ResNet50 (0.869, 95% CI: 0.866, 0.873), and ResNet101 (0.873, 95% CI: 0.869, 0.876). The time per inference step for the MobileNetV2 model (15 ms/step) was substantially lower than that of VGG16 (48 ms/step), ResNet50 (37 ms/step), and ResNet110 (56 ms/step). The visual comparisons between the model prediction and ground truth have demonstrated that the MobileNetV2 diagnostic models can identify very small cancerous nodes embedded in a large area of normal cells which is challenging for manual image analysis. Equally Important, the light weight MobleNetV2 models were computationally efficient and ready for mobile devices or devices of low computational power. These advances empower the development of a resource-efficient and high performing AI-based metastatic breast cancer diagnostic system that can adapt to under-resourced healthcare facilities in developing countries.

乳腺癌是导致癌症死亡的主要原因之一。发展中国家,特别是撒哈拉以南非洲、南亚和南美洲的乳腺癌患者死亡率是世界上最高的。造成全球死亡率差异的一个关键因素是,由于训练有素的病理学家严重短缺,导致诊断长期拖延,从而导致很大一部分患者在诊断时出现晚期症状。为了解决这一关键的医疗保健差距,我们开发了一种基于深度学习的转移性乳腺癌诊断系统,该系统可以实现高诊断准确性,以及适用于资源不足环境的计算效率和移动准备。我们评估了4种卷积神经网络(CNN)架构:MobileNetV2、VGG16、ResNet50和ResNet101。基于mobilenetv2的诊断模型在诊断精度、模型泛化和模型训练效率方面优于更复杂的VGG16、ResNet50和ResNet101模型。MobilenetV2的ROC AUC (0.933, 95% CI: 0.930, 0.936)高于VGG16 (0.911, 95% CI: 0.908, 0.915)、ResNet50 (0.869, 95% CI: 0.866, 0.873)和ResNet101 (0.873, 95% CI: 0.869, 0.876)。MobileNetV2模型的每个推理步骤的时间(15 ms/步)大大低于VGG16 (48 ms/步),ResNet50 (37 ms/步)和ResNet110 (56 ms/步)。模型预测和实际情况之间的视觉比较表明,MobileNetV2诊断模型可以识别嵌入在大面积正常细胞中的非常小的癌节点,这对于人工图像分析来说是具有挑战性的。同样重要的是,轻量级的MobleNetV2模型具有计算效率,可用于移动设备或低计算能力的设备。这些进步有助于开发资源高效和高性能的基于人工智能的转移性乳腺癌诊断系统,以适应发展中国家资源不足的医疗机构。
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引用次数: 2
期刊
Cancer Informatics
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