Rongjun Tang, Qing Yao, Ke Zhang, Qingqing Yu, Jun Lou, Lingdi Li
{"title":"革命性的预后:引入细胞死亡指数(CDI)作为CSCC患者的强大预后工具。","authors":"Rongjun Tang, Qing Yao, Ke Zhang, Qingqing Yu, Jun Lou, Lingdi Li","doi":"10.1002/tox.24265","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Cervical squamous cell carcinoma (CSCC) threatens the body health of women worldwide. This study aimed to foster a new concept of prognostic indicator named cell death index (CDI).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>RNA-seq and scRNA-seq datasets were downloaded from the GEO and TCGA database as the training and validation cohorts. Programmed cell death (PCD)-related gene signatures were obtained from published research. The construction of prognostic model was performed based on CDI value. Patients with CSCC were divided into high- and low-CDI groups. We explored the differences in overall survival time, immune infiltration, mutation status, and drug sensitivity between high and low CDI groups by R software.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We constructed prognostic model to calculate the CDI value with 23 genes. Patients with high CDI have shorter survival time than those with low CDI. CDI was considered a risk factor compared to other characteristics. The nomogram model estimated overall survival (OS) at 1, 3, and 6 years, with age, Stage, and CDI, indicating the accuracy of the model in predicting 1-, 3-, and 6-year survival rates. CDI values were negatively correlated with most immune checkpoint genes. We measured the significant drug sensitivity of Mitoxantrone, Sabutoclax, Sepantronium bromide, Topotecan, BI-2536, and BMS-754807 between high- and low-CDI groups with significant correlation.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This investigation constructed a novel effective prognostic indicator of CDI in patients with CSCC and identified potential genes associated with cell death that could be targeted for prognosis and treatment of CSCC.</p>\n </section>\n </div>","PeriodicalId":11756,"journal":{"name":"Environmental Toxicology","volume":"40 3","pages":"481-492"},"PeriodicalIF":3.2000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Revolutionizing prognosis: Introducing cell death index (CDI) as a powerful prognostic tool for CSCC patients\",\"authors\":\"Rongjun Tang, Qing Yao, Ke Zhang, Qingqing Yu, Jun Lou, Lingdi Li\",\"doi\":\"10.1002/tox.24265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Cervical squamous cell carcinoma (CSCC) threatens the body health of women worldwide. This study aimed to foster a new concept of prognostic indicator named cell death index (CDI).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>RNA-seq and scRNA-seq datasets were downloaded from the GEO and TCGA database as the training and validation cohorts. Programmed cell death (PCD)-related gene signatures were obtained from published research. The construction of prognostic model was performed based on CDI value. Patients with CSCC were divided into high- and low-CDI groups. We explored the differences in overall survival time, immune infiltration, mutation status, and drug sensitivity between high and low CDI groups by R software.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>We constructed prognostic model to calculate the CDI value with 23 genes. Patients with high CDI have shorter survival time than those with low CDI. CDI was considered a risk factor compared to other characteristics. The nomogram model estimated overall survival (OS) at 1, 3, and 6 years, with age, Stage, and CDI, indicating the accuracy of the model in predicting 1-, 3-, and 6-year survival rates. CDI values were negatively correlated with most immune checkpoint genes. 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Revolutionizing prognosis: Introducing cell death index (CDI) as a powerful prognostic tool for CSCC patients
Background
Cervical squamous cell carcinoma (CSCC) threatens the body health of women worldwide. This study aimed to foster a new concept of prognostic indicator named cell death index (CDI).
Methods
RNA-seq and scRNA-seq datasets were downloaded from the GEO and TCGA database as the training and validation cohorts. Programmed cell death (PCD)-related gene signatures were obtained from published research. The construction of prognostic model was performed based on CDI value. Patients with CSCC were divided into high- and low-CDI groups. We explored the differences in overall survival time, immune infiltration, mutation status, and drug sensitivity between high and low CDI groups by R software.
Results
We constructed prognostic model to calculate the CDI value with 23 genes. Patients with high CDI have shorter survival time than those with low CDI. CDI was considered a risk factor compared to other characteristics. The nomogram model estimated overall survival (OS) at 1, 3, and 6 years, with age, Stage, and CDI, indicating the accuracy of the model in predicting 1-, 3-, and 6-year survival rates. CDI values were negatively correlated with most immune checkpoint genes. We measured the significant drug sensitivity of Mitoxantrone, Sabutoclax, Sepantronium bromide, Topotecan, BI-2536, and BMS-754807 between high- and low-CDI groups with significant correlation.
Conclusion
This investigation constructed a novel effective prognostic indicator of CDI in patients with CSCC and identified potential genes associated with cell death that could be targeted for prognosis and treatment of CSCC.
期刊介绍:
The journal publishes in the areas of toxicity and toxicology of environmental pollutants in air, dust, sediment, soil and water, and natural toxins in the environment.Of particular interest are:
Toxic or biologically disruptive impacts of anthropogenic chemicals such as pharmaceuticals, industrial organics, agricultural chemicals, and by-products such as chlorinated compounds from water disinfection and waste incineration;
Natural toxins and their impacts;
Biotransformation and metabolism of toxigenic compounds, food chains for toxin accumulation or biodegradation;
Assays of toxicity, endocrine disruption, mutagenicity, carcinogenicity, ecosystem impact and health hazard;
Environmental and public health risk assessment, environmental guidelines, environmental policy for toxicants.