Pub Date : 2025-01-10eCollection Date: 2024-12-01DOI: 10.1515/jtim-2024-0037
Wanwan Zhang, Erlan Yu, Wenbo Zhao, Chuanjie Wu, Xunming Ji
{"title":"Secondary prevention for intracranial atherosclerotic stenosis: Where we stand and challenges ahead.","authors":"Wanwan Zhang, Erlan Yu, Wenbo Zhao, Chuanjie Wu, Xunming Ji","doi":"10.1515/jtim-2024-0037","DOIUrl":"10.1515/jtim-2024-0037","url":null,"abstract":"","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"537-539"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10eCollection Date: 2024-12-01DOI: 10.1515/jtim-2024-0034
Li Zhang, Jing Li
{"title":"Prospects for the application of artificial intelligence in geriatrics.","authors":"Li Zhang, Jing Li","doi":"10.1515/jtim-2024-0034","DOIUrl":"10.1515/jtim-2024-0034","url":null,"abstract":"","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"531-533"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background and objectives: Prior studies have highlighted an escalating global burden of hepatocellular carcinoma (HCC). The Notch signaling pathway regulates the initiation and development of HCC and determines the HCC prognosis.
Methods: The expression data of genes related to the Notch signaling pathway were acquired from public databases. To filter prognostic gene signatures and establish the risk model, the analyses of consensus clustering, least absolute shrinkage and selection operator (LASSO), and multivariate Cox were conducted. Subsequently, the risk stratification was optimized using a decision tree and nomogram. The immune landscapes were revealed utilizing the single-sample gene set enrichment analysis, and tumor immune dysfunction and exclusion score.
Results: According to the mRNA expression profile of Notch signaling pathway-related genes, HCC patients were stratified to three clusters, which have different survival probability and immune infiltration characteristic. Subsequently, we developed a risk model based on five prognostic Notch signaling-related gene signatures (SPP1, SMG5, HMMR, PLOD2, and CFHR4). The model demonstrated an accurate estimation of overall survival, revealing alterations in immune status and immunotherapy sensitivity among HCC patients with different risk scores.
Conclusions: This study constructed a Notch signaling pathway-related prognostic model, offering valuable insights for the assessment of immune characteristics and immunotherapy responses in HCC patients.
{"title":"A Notch signaling pathway-related gene signature: Characterizing the immune microenvironment and predicting prognosis in hepatocellular carcinoma.","authors":"Qingmiao Shi, Shuwen Jiang, Yifan Zeng, Xin Yuan, Yaqi Zhang, Qingfei Chu, Chen Xue, Lanjuan Li","doi":"10.1515/jtim-2024-0020","DOIUrl":"10.1515/jtim-2024-0020","url":null,"abstract":"<p><strong>Background and objectives: </strong>Prior studies have highlighted an escalating global burden of hepatocellular carcinoma (HCC). The Notch signaling pathway regulates the initiation and development of HCC and determines the HCC prognosis.</p><p><strong>Methods: </strong>The expression data of genes related to the Notch signaling pathway were acquired from public databases. To filter prognostic gene signatures and establish the risk model, the analyses of consensus clustering, least absolute shrinkage and selection operator (LASSO), and multivariate Cox were conducted. Subsequently, the risk stratification was optimized using a decision tree and nomogram. The immune landscapes were revealed utilizing the single-sample gene set enrichment analysis, and tumor immune dysfunction and exclusion score.</p><p><strong>Results: </strong>According to the mRNA expression profile of Notch signaling pathway-related genes, HCC patients were stratified to three clusters, which have different survival probability and immune infiltration characteristic. Subsequently, we developed a risk model based on five prognostic Notch signaling-related gene signatures (SPP1, SMG5, HMMR, PLOD2, and CFHR4). The model demonstrated an accurate estimation of overall survival, revealing alterations in immune status and immunotherapy sensitivity among HCC patients with different risk scores.</p><p><strong>Conclusions: </strong>This study constructed a Notch signaling pathway-related prognostic model, offering valuable insights for the assessment of immune characteristics and immunotherapy responses in HCC patients.</p>","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"553-568"},"PeriodicalIF":7.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10eCollection Date: 2024-12-01DOI: 10.1515/jtim-2024-0033
Xuan Li, Aoran Liu, Xuechen Mu, Zhihang Wang, Jun Xiao, Yinwei Qu, Zhenyu Huang, Ye Zhang, Ying Xu
Background and objectives: Cholangiocarcinoma (CHOL) is a rare and highly aggressive cancer that originates in the bile duct; it has an average five-year survival rate of 9%, which makes it the cancer with the lowest survival rate among all 33 cancer types in the cancer genome atlas (TCGA) Program. The aim of this study is to elucidate the key determinants of the high malignancy level of CHOL through computational and cell-based experimental approaches and, particularly, to investigate how bile acids (BAs) influence CHOL's propensity to metastasize.
Methods: Our study analyzed the transcriptomic data from 1835 tissue samples of 7 digestive system cancer types in the TCGA database and compared them with those of 330 control tissue samples. Multiple cellular and molecular factors were considered in the study, including the level of hypoxia, level of immune cell infiltration, degree of cellular dedifferentiation, and level of sialic acid (SA) accumulation on the surface of cancer cells. Using these factors, we developed a multivariable regression model for the five-year survival rate, as reported by the Surveillance, Epidemiology, and End Results (SEER) Program reports, and analyzed how BA biology influences a few of these factors and causes CHOL to have a high malignancy level.
Results: CHOL exhibited the highest level of SA accumulation and B-cell infiltration among all cancer types studied. BAs inhibit the cell cycle progression through the receptor GPBAR1, thereby limiting the rate of nucleotide biosynthesis-which in turn forces the cells to increase SA biosynthesis in order to maintain the intracellular pH at a stable level-thereby driving cell migration and metastasis, as established in our previous study.
Conclusions: BAs are the key contributors to the lowest five-year survival rate of CHOL among the seven cancer types studied here. This finding not only reveals the molecular mechanisms underlying the high malignancy level of CHOL but also provides a new potential target for the diagnosis and treatment of CHOL.
{"title":"Computational analyses to reveal the key determinants of the high malignancy level of cholangiocarcinoma.","authors":"Xuan Li, Aoran Liu, Xuechen Mu, Zhihang Wang, Jun Xiao, Yinwei Qu, Zhenyu Huang, Ye Zhang, Ying Xu","doi":"10.1515/jtim-2024-0033","DOIUrl":"10.1515/jtim-2024-0033","url":null,"abstract":"<p><strong>Background and objectives: </strong>Cholangiocarcinoma (CHOL) is a rare and highly aggressive cancer that originates in the bile duct; it has an average five-year survival rate of 9%, which makes it the cancer with the lowest survival rate among all 33 cancer types in the cancer genome atlas (TCGA) Program. The aim of this study is to elucidate the key determinants of the high malignancy level of CHOL through computational and cell-based experimental approaches and, particularly, to investigate how bile acids (BAs) influence CHOL's propensity to metastasize.</p><p><strong>Methods: </strong>Our study analyzed the transcriptomic data from 1835 tissue samples of 7 digestive system cancer types in the TCGA database and compared them with those of 330 control tissue samples. Multiple cellular and molecular factors were considered in the study, including the level of hypoxia, level of immune cell infiltration, degree of cellular dedifferentiation, and level of sialic acid (SA) accumulation on the surface of cancer cells. Using these factors, we developed a multivariable regression model for the five-year survival rate, as reported by the Surveillance, Epidemiology, and End Results (SEER) Program reports, and analyzed how BA biology influences a few of these factors and causes CHOL to have a high malignancy level.</p><p><strong>Results: </strong>CHOL exhibited the highest level of SA accumulation and B-cell infiltration among all cancer types studied. BAs inhibit the cell cycle progression through the receptor <i>GPBAR1</i>, thereby limiting the rate of nucleotide biosynthesis-which in turn forces the cells to increase SA biosynthesis in order to maintain the intracellular pH at a stable level-thereby driving cell migration and metastasis, as established in our previous study.</p><p><strong>Conclusions: </strong>BAs are the key contributors to the lowest five-year survival rate of CHOL among the seven cancer types studied here. This finding not only reveals the molecular mechanisms underlying the high malignancy level of CHOL but also provides a new potential target for the diagnosis and treatment of CHOL.</p>","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"602-617"},"PeriodicalIF":7.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10eCollection Date: 2024-12-01DOI: 10.1515/jtim-2024-0036
Shantong Jiang, Hongyan Shi, Yanqing Hu, Ning Zhang, Hongyu Wang
{"title":"Effectiveness and safety of Qixuekang Oral Liquid on vascular health.","authors":"Shantong Jiang, Hongyan Shi, Yanqing Hu, Ning Zhang, Hongyu Wang","doi":"10.1515/jtim-2024-0036","DOIUrl":"10.1515/jtim-2024-0036","url":null,"abstract":"","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"618-620"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10eCollection Date: 2024-12-01DOI: 10.1515/jtim-2024-0021
Cheng Xue, Jiayi Lv, Bo Yang, Shuqin Mei, Jing Xu, Xinming Li, Liming Zhang, Zhiguo Mao
Polycystic kidney disease (PKD) is a genetic disorder marked by numerous cysts in the kidneys, progressively impairing renal function. It is classified into autosomal dominant polycystic kidney disease (ADPKD) and autosomal recessive polycystic kidney disease (ARPKD), with ADPKD being more common. Current treatments mainly focus on symptom relief and slowing disease progression, without offering a cure. Recent advancements in gene editing technologies, such as CRISPR-Cas9, have introduced new therapeutic possibilities for PKD. These approaches include miR-17 antisense oligonucleotides, adenovirus-mediated gene knockdown, Pkd1 gene or polycystin -1 C-terminal tail enhancement therapy, and 3-UTR miR-17 binding element by CRISPR-Cas9, which have shown potential in animal models and early clinical trials. Specifically for ARPKD, strategies like antisense oligonucleotide therapy targeting c-myc and CRISPR/ Cas9 knockdown of the P2rx7 gene have shown promise. Despite facing challenges such as technological limitations, ethical and legal issues, and high costs, gene therapy presents unprecedented hope for PKD treatment. Future interdisciplinary collaboration and international cooperation are essential for developing more effective treatment strategies for PKD patients.
{"title":"Gene therapy in polycystic kidney disease: A promising future.","authors":"Cheng Xue, Jiayi Lv, Bo Yang, Shuqin Mei, Jing Xu, Xinming Li, Liming Zhang, Zhiguo Mao","doi":"10.1515/jtim-2024-0021","DOIUrl":"10.1515/jtim-2024-0021","url":null,"abstract":"<p><p>Polycystic kidney disease (PKD) is a genetic disorder marked by numerous cysts in the kidneys, progressively impairing renal function. It is classified into autosomal dominant polycystic kidney disease (ADPKD) and autosomal recessive polycystic kidney disease (ARPKD), with ADPKD being more common. Current treatments mainly focus on symptom relief and slowing disease progression, without offering a cure. Recent advancements in gene editing technologies, such as CRISPR-Cas9, have introduced new therapeutic possibilities for PKD. These approaches include miR-17 antisense oligonucleotides, adenovirus-mediated gene knockdown, Pkd1 gene or polycystin -1 C-terminal tail enhancement therapy, and 3-UTR miR-17 binding element by CRISPR-Cas9, which have shown potential in animal models and early clinical trials. Specifically for ARPKD, strategies like antisense oligonucleotide therapy targeting c-myc and CRISPR/ Cas9 knockdown of the P2rx7 gene have shown promise. Despite facing challenges such as technological limitations, ethical and legal issues, and high costs, gene therapy presents unprecedented hope for PKD treatment. Future interdisciplinary collaboration and international cooperation are essential for developing more effective treatment strategies for PKD patients.</p>","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"543-552"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background and objectives: The association between chronic kidney disease (CKD) and cardiovascular disease has been previously evaluated. This study aimed to evaluate the association between the American Heart Association's Life's Essential 8 (LE8) and the prevalence and all-cause mortality of CKD in a nationally representative population of adults in the US.
Methods: This retrospective analysis included participants from the National Health and Nutrition Examination Survey spanning 2015-2018. We used multivariable survey logistic regression model to calculate the adjusted odds ratios (AORs) of the LE8 score for the prevalence of CKD. Survey-weighted Cox proportional hazards models were used to calculate the adjusted hazards ratios (AHRs) of the LE8 score for the risk of all-cause mortality among participants with CKD.
Results: Of the 8907 included participants, 789 had stage 3 to 5 CKD, and 8118 were in the non-CKD group. The adjusted prevalence rate of CKD was 10.7% in the low LE8 score group, and lower in the moderate (7.9%) and high (7.7%) LE8 score groups. Compared with low LE8 scores, moderate LE8 score (adjusted odds ratio [AOR] 0.628, 95% confidence interval [CI]: 0.463 to 0.853, P = 0.004) and high LE8 scores (AOR 0.328, 95% CI: 0.142 to 0.759, P = 0.011) were associated with lower prevalence rates of CKD. A similar association was found for health factors scores. Additionally, an increase in the LE8 score was associated with a lower risk of all-cause mortality (adjusted hazard ratio [AHR] 0.702, 95% CI: 0.594 to 0.829, P < 0.001).
Conclusion: The results of this study suggest the association of higher LE8 and its subscale scores with a lower prevalence and all-cause mortality of CKD.
背景和目的:慢性肾脏疾病(CKD)和心血管疾病之间的关联已经被评估过。本研究旨在评估美国心脏协会的生命基本8 (LE8)与美国全国代表性成人CKD患病率和全因死亡率之间的关系。方法:本回顾性分析纳入了2015-2018年全国健康与营养检查调查的参与者。我们使用多变量调查logistic回归模型来计算LE8评分与CKD患病率的校正优势比(AORs)。使用调查加权Cox比例风险模型计算CKD参与者全因死亡风险的LE8评分的调整风险比(AHRs)。结果:在纳入的8907名参与者中,789名为3至5期CKD, 8118名为非CKD组。低LE8评分组CKD校正患病率为10.7%,中LE8评分组为7.9%,高LE8评分组为7.7%。与低LE8评分相比,中等LE8评分(调整优势比[AOR] 0.628, 95%可信区间[CI]: 0.463 ~ 0.853, P = 0.004)和高LE8评分(调整优势比[AOR] 0.328, 95% CI: 0.142 ~ 0.759, P = 0.011)与低CKD患病率相关。健康因素得分也有类似的关联。此外,LE8评分的增加与全因死亡风险的降低相关(校正风险比[AHR] 0.702, 95% CI: 0.594 ~ 0.829, P < 0.001)。结论:本研究结果提示高LE8及其亚量表评分与较低的CKD患病率和全因死亡率相关。
{"title":"Association of life's essential 8 with prevalence and all-cause mortality of chronic kidney disease among US adults: Results from the National Health and Nutrition Examination Survey (2015-2018).","authors":"Wei Chen, Yuanjun Tang, Yachen Si, Boxiang Tu, Fuchuan Xiao, Xiaolu Bian, Ying Xu, Yingyi Qin","doi":"10.1515/jtim-2023-0119","DOIUrl":"10.1515/jtim-2023-0119","url":null,"abstract":"<p><strong>Background and objectives: </strong>The association between chronic kidney disease (CKD) and cardiovascular disease has been previously evaluated. This study aimed to evaluate the association between the American Heart Association's Life's Essential 8 (LE8) and the prevalence and all-cause mortality of CKD in a nationally representative population of adults in the US.</p><p><strong>Methods: </strong>This retrospective analysis included participants from the National Health and Nutrition Examination Survey spanning 2015-2018. We used multivariable survey logistic regression model to calculate the adjusted odds ratios (AORs) of the LE8 score for the prevalence of CKD. Survey-weighted Cox proportional hazards models were used to calculate the adjusted hazards ratios (AHRs) of the LE8 score for the risk of all-cause mortality among participants with CKD.</p><p><strong>Results: </strong>Of the 8907 included participants, 789 had stage 3 to 5 CKD, and 8118 were in the non-CKD group. The adjusted prevalence rate of CKD was 10.7% in the low LE8 score group, and lower in the moderate (7.9%) and high (7.7%) LE8 score groups. Compared with low LE8 scores, moderate LE8 score (adjusted odds ratio [AOR] 0.628, 95% confidence interval [CI]: 0.463 to 0.853, <i>P</i> = 0.004) and high LE8 scores (AOR 0.328, 95% CI: 0.142 to 0.759, <i>P</i> = 0.011) were associated with lower prevalence rates of CKD. A similar association was found for health factors scores. Additionally, an increase in the LE8 score was associated with a lower risk of all-cause mortality (adjusted hazard ratio [AHR] 0.702, 95% CI: 0.594 to 0.829, <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>The results of this study suggest the association of higher LE8 and its subscale scores with a lower prevalence and all-cause mortality of CKD.</p>","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"581-591"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10eCollection Date: 2024-12-01DOI: 10.1515/jtim-2023-0133
Kui Wang, Lingying Zhao, Tianyi Che, Chunhua Zhou, Xianzheng Qin, Yu Hong, Weitong Gao, Ling Zhang, Yubei Gu, Duowu Zou
<p><strong>Background and objectives: </strong>Primary colorectal lymphoma (PCL) is an infrequently occurring form of cancer, with the elderly population exhibiting an increasing prevalence of the disease. Furthermore, advanced age is associated with a poorer prognosis. Accurate prognostication is essential for the treatment of individuals diagnosed with PCL. However, no reliable predictive survival model exists for elderly patients with PCL. Therefore, this study aimed to develop an individualized survival prediction model for elderly patients with PCL and stratify its risk to aid in the treatment and monitoring of patients.</p><p><strong>Methods: </strong>Patients aged 60 or older with PCL from 1975 to 2013 in the Surveillance, Epidemiology, and End Results database were selected and randomly divided into a training cohort (<i>n</i> = 1305) and a validation cohort (<i>n</i> = 588). The patients from 2014-2015 (<i>n</i> = 207) were used for external validation. The research team utilized both Cox regression and the least absolute shrinkage and selection operator (LASSO) regression to analyze potential predictors, in order to identify the most suitable model for constructing an OS-nomogram and an associated network version. The risk stratification is constructed on the basis of this model. The performance of the model was evaluated based on the consistency index (C-index), calibration curve, and decision curve analysis (DCA) to determine its resolving power and calibration capability.</p><p><strong>Results: </strong>Age, gender, marital status, Ann Arbor staging, primary site, surgery, histological type, and chemotherapy were independent predictors of Overall Survival (OS) and were therefore included in our nomogram. The Area Under the Curve (AUC) of the 1, 3, and 5-year OS in the training, validation, and external validation sets ranged from 0.732 to 0.829. The Receiver Operating Characteristic (ROC) curves showed that the nomogram model outperformed the Ann Arbor stage system when predicting elderly patients with PCL prognosis at 1, 3, and 5 years in the training set, validation dataset, and external validation cohort. The Concordance Index (C-index) also demonstrated that the nomogram had excellent predictive accuracy and robustness. The calibration curves demonstrated a strong agreement between observed and predicted values. In the external validation cohort, the C-index (0.769, 95%CI: 0.712-0.826) and calibration curves of 1000 bootstrap samples also indicated a high level of concordance between observed and predicted values. The nomogram-related DCA curves exhibited superior clinical utility when compared to Ann Arbor stage. Furthermore, an online prediction tool for overall survival has been developed: https://medkuiwang.shinyapps.io/DynNomapp/.</p><p><strong>Conclusion: </strong>This was the first study to construct and validate predictive survival nomograms for elderly patients with PCL, which is better than the Ann Arbor stage. It will
{"title":"Development and validation of web-based risk score predicting prognostic nomograms for elderly patients with primary colorectal lymphoma: A population-based study.","authors":"Kui Wang, Lingying Zhao, Tianyi Che, Chunhua Zhou, Xianzheng Qin, Yu Hong, Weitong Gao, Ling Zhang, Yubei Gu, Duowu Zou","doi":"10.1515/jtim-2023-0133","DOIUrl":"10.1515/jtim-2023-0133","url":null,"abstract":"<p><strong>Background and objectives: </strong>Primary colorectal lymphoma (PCL) is an infrequently occurring form of cancer, with the elderly population exhibiting an increasing prevalence of the disease. Furthermore, advanced age is associated with a poorer prognosis. Accurate prognostication is essential for the treatment of individuals diagnosed with PCL. However, no reliable predictive survival model exists for elderly patients with PCL. Therefore, this study aimed to develop an individualized survival prediction model for elderly patients with PCL and stratify its risk to aid in the treatment and monitoring of patients.</p><p><strong>Methods: </strong>Patients aged 60 or older with PCL from 1975 to 2013 in the Surveillance, Epidemiology, and End Results database were selected and randomly divided into a training cohort (<i>n</i> = 1305) and a validation cohort (<i>n</i> = 588). The patients from 2014-2015 (<i>n</i> = 207) were used for external validation. The research team utilized both Cox regression and the least absolute shrinkage and selection operator (LASSO) regression to analyze potential predictors, in order to identify the most suitable model for constructing an OS-nomogram and an associated network version. The risk stratification is constructed on the basis of this model. The performance of the model was evaluated based on the consistency index (C-index), calibration curve, and decision curve analysis (DCA) to determine its resolving power and calibration capability.</p><p><strong>Results: </strong>Age, gender, marital status, Ann Arbor staging, primary site, surgery, histological type, and chemotherapy were independent predictors of Overall Survival (OS) and were therefore included in our nomogram. The Area Under the Curve (AUC) of the 1, 3, and 5-year OS in the training, validation, and external validation sets ranged from 0.732 to 0.829. The Receiver Operating Characteristic (ROC) curves showed that the nomogram model outperformed the Ann Arbor stage system when predicting elderly patients with PCL prognosis at 1, 3, and 5 years in the training set, validation dataset, and external validation cohort. The Concordance Index (C-index) also demonstrated that the nomogram had excellent predictive accuracy and robustness. The calibration curves demonstrated a strong agreement between observed and predicted values. In the external validation cohort, the C-index (0.769, 95%CI: 0.712-0.826) and calibration curves of 1000 bootstrap samples also indicated a high level of concordance between observed and predicted values. The nomogram-related DCA curves exhibited superior clinical utility when compared to Ann Arbor stage. Furthermore, an online prediction tool for overall survival has been developed: https://medkuiwang.shinyapps.io/DynNomapp/.</p><p><strong>Conclusion: </strong>This was the first study to construct and validate predictive survival nomograms for elderly patients with PCL, which is better than the Ann Arbor stage. It will ","PeriodicalId":51339,"journal":{"name":"Journal of Translational Internal Medicine","volume":"12 6","pages":"569-580"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}