Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.026781
Xiaojun Zhou, Mengxue Liu, Linlin Song
{"title":"Structural characterization of four Rhododendron spp. chloroplast genomes and comparative analyses with other azaleas","authors":"Xiaojun Zhou, Mengxue Liu, Linlin Song","doi":"10.32604/biocell.2023.026781","DOIUrl":"https://doi.org/10.32604/biocell.2023.026781","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"159 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77484615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.026049
Hongmei Wu, Di Li, Yuanyuan Huang, Ruyuan Liu, Xiaonian Zhu
{"title":"Research progress of protein phosphatase 2A in cellular autophagy","authors":"Hongmei Wu, Di Li, Yuanyuan Huang, Ruyuan Liu, Xiaonian Zhu","doi":"10.32604/biocell.2023.026049","DOIUrl":"https://doi.org/10.32604/biocell.2023.026049","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"15 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72535110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.027308
Li Chen, Fangfang Li, Shouyan Cao, Xia Li, Chao Zhou, Sai Han, Youzhong Zhang
{"title":"RASAL2 acts as a tumor suppressor in cervical cancer cells","authors":"Li Chen, Fangfang Li, Shouyan Cao, Xia Li, Chao Zhou, Sai Han, Youzhong Zhang","doi":"10.32604/biocell.2023.027308","DOIUrl":"https://doi.org/10.32604/biocell.2023.027308","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"15 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72700349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.027718
{"title":"Analysis of tumor-draining vein secretome: A direct access to tumor-derived extracellular vesicles in surgical lung cancer patients","authors":"","doi":"10.32604/biocell.2023.027718","DOIUrl":"https://doi.org/10.32604/biocell.2023.027718","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"15 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78181883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.030796
ZHAOXU YAO, HAIBIN MA, LIN LIU, QIAN ZHAO, LONGCHAO QIN, XUEYAN REN, CHUANJUN WU, KAILI SUN
Objective: Through integrated bioinformatics analysis, the goal of this work was to find new, characterised N7-methylguanosine modification-related long non-coding RNAs (m7G-lncRNAs) that might be used to predict the prognosis of laryngeal squamous cell carcinoma (LSCC). Methods: The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database & sanitised. Then, using co-expression analysis of m7G-associated mRNAs & lncRNAs & differential expression analysis (DEA) among LSCC & normal sample categories, we discovered lncRNAs that were connected to m7G. The prognosis prediction model was built for the training category using univariate & multivariate COX regression & LASSO regression analyses, & the model’s efficacy was checked against the test category data. In addition, we conducted DEA of prognostic m7G-lncRNAs among LSCC & normal sample categories & compiled a list of co-expression networks & the structure of prognosis m7G-lncRNAs. To compare the prognoses for individuals with LSCC in the high- & low-risk categories in the prognosis prediction model, survival and risk assessments were also carried out. Finally, we created a nomogram to accurately forecast the outcomes of LSCC patients & created receiver operating characteristic (ROC) curves to assess the prognosis prediction model’s predictive capability. Results: Using co-expression network analysis & differential expression analysis, we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs, respectively. We then constructed a prognosis prediction model for six m7G-lncRNAs (FLG−AS1, RHOA−IT1, AC020913.3, AC027307.2, AC010973.2 and AC010789.1), identified 32 DEPm7G-lncRNAs, analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs, and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients. By displaying ROC curves and a nomogram, we finally checked the prognosis prediction model's accuracy. Conclusion: By creating novel predictive lncRNA signatures for clinical diagnosis & therapy, our findings will contribute to understanding the pathogenetic process of LSCC.
{"title":"Novel defined N7-methylguanosine modification-related lncRNAs for predicting the prognosis of laryngeal squamous cell carcinoma","authors":"ZHAOXU YAO, HAIBIN MA, LIN LIU, QIAN ZHAO, LONGCHAO QIN, XUEYAN REN, CHUANJUN WU, KAILI SUN","doi":"10.32604/biocell.2023.030796","DOIUrl":"https://doi.org/10.32604/biocell.2023.030796","url":null,"abstract":"<b>Objective:</b> Through integrated bioinformatics analysis, the goal of this work was to find new, characterised N7-methylguanosine modification-related long non-coding RNAs (m7G-lncRNAs) that might be used to predict the prognosis of laryngeal squamous cell carcinoma (LSCC). <b>Methods:</b> The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database & sanitised. Then, using co-expression analysis of m7G-associated mRNAs & lncRNAs & differential expression analysis (DEA) among LSCC & normal sample categories, we discovered lncRNAs that were connected to m7G. The prognosis prediction model was built for the training category using univariate & multivariate COX regression & LASSO regression analyses, & the model’s efficacy was checked against the test category data. In addition, we conducted DEA of prognostic m7G-lncRNAs among LSCC & normal sample categories & compiled a list of co-expression networks & the structure of prognosis m7G-lncRNAs. To compare the prognoses for individuals with LSCC in the high- & low-risk categories in the prognosis prediction model, survival and risk assessments were also carried out. Finally, we created a nomogram to accurately forecast the outcomes of LSCC patients & created receiver operating characteristic (ROC) curves to assess the prognosis prediction model’s predictive capability. <b>Results:</b> Using co-expression network analysis & differential expression analysis, we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs, respectively. We then constructed a prognosis prediction model for six m7G-lncRNAs (<i>FLG−AS1</i>, <i>RHOA−IT1</i>, <i>AC020913.3</i>, <i>AC027307.2</i>, <i>AC010973.2</i> and <i>AC010789.1</i>), identified 32 DEPm7G-lncRNAs, analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs, and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients. By displaying ROC curves and a nomogram, we finally checked the prognosis prediction model's accuracy. <b>Conclusion:</b> By creating novel predictive lncRNA signatures for clinical diagnosis & therapy, our findings will contribute to understanding the pathogenetic process of LSCC.","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135755380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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