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}
Pub Date : 2023-01-01DOI: 10.32604/biocell.2023.s0124
{"title":"Abstracts from XXIV Annual Meeting of the Argentinean Society of Biology (SAB)","authors":"","doi":"10.32604/biocell.2023.s0124","DOIUrl":"https://doi.org/10.32604/biocell.2023.s0124","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"24 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83052457","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.2023.023553
K. Sun, Dongqin Wang, Zhiqiang Zhang, Yinlong Huang, Xiaofu Lian, Jia Hua, Jing Zhang, Chaoqun Lian
Columbianetin acetate (CE) is one of the effective components of Angelica pubescens. So far, the specific role and molecular mechanism of CE in pancreatic cancer are not clear. Thus, this study aimed to explore the specific mechanism of CE on pancreatic cancer. The target genes combined with CE were predicted through the PharmMapper database and the 3D molecular structure of CE. Then, the Cancer Genome Atlas (TCGA) and Cistrome data browser (DB) databases were used to screen Meiotic nuclear divisions 1 (MND1)-related genes, transcription factors, and transcription factor data sets, and the intersection of the above data sets. The “limma” package in the R and gene expression profiling interactive analysis (GEPIA) databases were used to analyze the correlation and survival difference between the target genes and MND1 to predict the degree of association between CE and MND1. Western blotting and RT-PCR experiments revealed the regulatory relationship among CE, E2F1, and MND1 at the cellular level. The specific effects of CE on pancreatic cancer cells were explored through CCK8, wound healing, migration, and flow cycle experiments. E2F1, also the predictive transcription factor of MND1, was also the predictive target protein of CE. At the same time, E2F1 and MND1 were closely related in pancreatic tissue. In the cell function experiment, CE and interference with E2F1 expression could reduce the gene and protein expression of MND1, which was closely associated with cell proliferation, migration, and cycle development. Similarly, interfering with the expression of mnd1 can also inhibit the further development of tumor cells. CE may inhibit the development of pancreatic cancer cells by reducing the expression of MND1. This implies that CE may be a potential novel agent for the treatment of pancreatic cancer.
乙酸柱莲素是当归的有效成分之一。迄今为止，CE在胰腺癌中的具体作用和分子机制尚不清楚。因此，本研究旨在探讨CE治疗胰腺癌的具体机制。通过PharmMapper数据库和CE的三维分子结构预测与CE结合的靶基因。然后，利用Cancer Genome Atlas (TCGA)和Cistrome data browser (DB)数据库筛选MND1相关基因、转录因子、转录因子数据集以及上述数据集的交集。利用R和基因表达谱交互分析(GEPIA)数据库中的“limma”包分析靶基因与MND1的相关性和生存差异，预测CE与MND1的关联程度。Western blotting和RT-PCR实验揭示了CE、E2F1和MND1在细胞水平上的调控关系。通过CCK8、伤口愈合、迁移和血流循环实验探讨CE对胰腺癌细胞的特异性影响。E2F1也是MND1的预测转录因子，也是CE的预测靶蛋白。同时，E2F1与MND1在胰腺组织中密切相关。在细胞功能实验中，CE和干扰E2F1表达可降低与细胞增殖、迁移和周期发育密切相关的MND1基因和蛋白表达。同样，干扰mnd1的表达也可以抑制肿瘤细胞的进一步发展。CE可能通过降低MND1的表达来抑制胰腺癌细胞的发展。这表明CE可能是一种潜在的治疗胰腺癌的新型药物。
{"title":"Columbianetin acetate inhibits the occurrence and development of pancreatic cancer cells by down-regulating the expression of Meiotic nuclear divisions 1","authors":"K. Sun, Dongqin Wang, Zhiqiang Zhang, Yinlong Huang, Xiaofu Lian, Jia Hua, Jing Zhang, Chaoqun Lian","doi":"10.32604/biocell.2023.023553","DOIUrl":"https://doi.org/10.32604/biocell.2023.023553","url":null,"abstract":"Columbianetin acetate (CE) is one of the effective components of Angelica pubescens. So far, the specific role and molecular mechanism of CE in pancreatic cancer are not clear. Thus, this study aimed to explore the specific mechanism of CE on pancreatic cancer. The target genes combined with CE were predicted through the PharmMapper database and the 3D molecular structure of CE. Then, the Cancer Genome Atlas (TCGA) and Cistrome data browser (DB) databases were used to screen Meiotic nuclear divisions 1 (MND1)-related genes, transcription factors, and transcription factor data sets, and the intersection of the above data sets. The “limma” package in the R and gene expression profiling interactive analysis (GEPIA) databases were used to analyze the correlation and survival difference between the target genes and MND1 to predict the degree of association between CE and MND1. Western blotting and RT-PCR experiments revealed the regulatory relationship among CE, E2F1, and MND1 at the cellular level. The specific effects of CE on pancreatic cancer cells were explored through CCK8, wound healing, migration, and flow cycle experiments. E2F1, also the predictive transcription factor of MND1, was also the predictive target protein of CE. At the same time, E2F1 and MND1 were closely related in pancreatic tissue. In the cell function experiment, CE and interference with E2F1 expression could reduce the gene and protein expression of MND1, which was closely associated with cell proliferation, migration, and cycle development. Similarly, interfering with the expression of mnd1 can also inhibit the further development of tumor cells. CE may inhibit the development of pancreatic cancer cells by reducing the expression of MND1. This implies that CE may be a potential novel agent for the treatment of pancreatic cancer.","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"100 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83348294","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.2023.027103
Xi Yang, Huixian Wu, Chao-Liang Xiong, Bo Zhao, Meilian Liu, J. Qin, Meihai Deng
{"title":"Comprehensive bioinformatics analysis of CYB561 expression in breast cancer: Link between prognosis and immune infiltration","authors":"Xi Yang, Huixian Wu, Chao-Liang Xiong, Bo Zhao, Meilian Liu, J. Qin, Meihai Deng","doi":"10.32604/biocell.2023.027103","DOIUrl":"https://doi.org/10.32604/biocell.2023.027103","url":null,"abstract":"","PeriodicalId":55384,"journal":{"name":"Biocell","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90194699","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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