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1.

001-es BibID:BIBFORM108788
035-os BibID:(scopus)85149565612 (wos)000941641100001
Első szerző:Li, Zhangzuo
Cím:Lactate in the tumor microenvironment : a rising star for targeted tumor therapy / Zhangzuo Li, Qi Wang, Xufeng Huang, Mengting Yang, Shujing Zhou, Zhengrui Li, Zhengzou Fang, Yidan Tang, Qian Chen, Hanjin Hou, Li Li, Fei Fei, Qiaowei Wang, Yuqing Wu, Aihua Gong
Dátum:2023
ISSN:2296-861X
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Frontiers in Nutrition. - 10 (2023), p. 1-10. -
További szerzők:Wang, Qi Huang, Xufeng (1997-) (Bioinformatics, Oncology, Dentistry) Yang, Mengting Zhou, Shujing (1997-) Li, Zhengrui Fang, Zhengzou Tang, Yidan Chen, Qian Hou, Hanjin Li, Li Fei, Fei Wang, Qiaowei Wu, Yuqing Gong, Aihua
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2.

001-es BibID:BIBFORM111624
035-os BibID:(Scopus)85159798082 (WoS)000990535700001
Első szerző:Wang, Qi
Cím:Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation / Qi Wang, Ying Liu, Zhangzuo Li, Yidan Tang, Weiguo Long, Huaiyu Xin, Xufeng Huang, Shujing Zhou, Longbin Wang, Bochuan Liang, Zhengrui Li, Min Xu
Dátum:2023
ISSN:1664-3224
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Frontiers in Immunology. - 14 (2023), p. 1-14. -
További szerzők:Liu, Ying Li, Zhangzuo Tang, Yidan Long, Weiguo Xin, Huaiyu Huang, Xufeng (1997-) (Bioinformatics, Oncology, Dentistry) Zhou, Shujing (1997-) Wang, Longbin Liang, Bochuan Li, Zhengrui Xu, Min
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3.

001-es BibID:BIBFORM107920
035-os BibID:(Scopus)85147762684
Első szerző:Wang, Qi
Cím:NCAPG2 could be an immunological and prognostic biomarker : from pan-cancer analysis to pancreatic cancer validation / Qi Wang, Zhangzuo Li, Shujing Zhou, Zhengrui Li, Xufeng Huang, Yiwei He, Yuhan Zhang, Xiaoxian Zhao, Yidan Tang, Min Xu
Dátum:2023
ISSN:1664-3224
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Frontiers in Immunology. - 14 (2023), p. 1-16. -
További szerzők:Li, Zhangzuo Zhou, Shujing (1997-) Li, Zhengrui Huang, Xufeng (1997-) (Bioinformatics, Oncology, Dentistry) He, Yiwei Zhang, Yuhan Zhao, Xiaoxian Tang, Yidan Xu, Min
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4.

001-es BibID:BIBFORM114537
035-os BibID:(Scopus)85169567398 (WOS)001055523200001
Első szerző:Zhang, Danfeng
Cím:GLS as a diagnostic biomarker in breast cancer : in-silico, in-situ, and in-vitro insights / Danfeng Zhang, Man Wang, Xufeng Huang, Longbin Wang, Ying Liu, Shujing Zhou, Yidan Tang, Qi Wang, Zhengrui Li, Geng Wang
Dátum:2023
ISSN:2234-943X
Megjegyzések:Background: Recently, a novel programmed cell death mechanism, Cuproptosis, has been discovered and found to play an important role in the development and progression of diverse tumors. In the present study, we comprehensively investigated the core gene of this mechanism, GLS, in breast cancer. Materials and methods: Bulk RNA sequencing data were curated from the TCGA repository to investigate the aberrant expression of GLS over diverse cancer types. Then, we examined its efficacy as a diagnostic biomarker in breast cancer by Area Under Curve (AUC) of the Receiver Operative Characteristic (ROC) curve. Furthermore, by applying siRNA technique, we knocked down the GLS expression level in cancerous cell lines, measuring the corresponding effects on cell proliferation and metastasis. Afterward, we explored the potential implications of GLS expression in the tumor immune microenvironment quantitatively by using several R packages and algorithms, including ESTIMATE, CIBERSORT, etc. Results: Pan-cancer analysis suggested that GLS was aberrantly over-expressed in many cancer types, with breast cancer being typical. More in-depth analyses revealed the expression of GLS exerted a high ROC-AUC value in breast cancer diagnosis. Through the knock-down of GLS expression, it was found that GLS expression was strongly relevant to the growth and metastasis of tumor. Furthermore, it was also found to be correlated with the immune tumor microenvironment. Conclusion: We highlighted that GLS expression might be applicable as a diagnostic biomarker in breast cancer and possess significant implications in the growth and metastasis of tumor and the immune tumor microenvironment, sharing new insights into ontological and personalized medicine.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
bioinformatics
biomarker
breast cancer
cuproptosis
EMT pathway
Megjelenés:Frontiers in Oncology. - 13 (2023), p. 1-8. -
További szerzők:Wang, Man Huang, Xufeng (1997-) (Bioinformatics, Oncology, Dentistry) Wang, Longbin Liu, Ying Zhou, Shujing (1997-) Tang, Yidan Wang, Qi Li, Zhengrui Wang, Geng
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5.

001-es BibID:BIBFORM122381
035-os BibID:(Scopus)85193773050
Első szerző:Zhou, Shujing
Cím:Comprehensive bioinformatics analytics and in vivo validation reveal SLC31A1 as an emerging diagnostic biomarker for acute myocardial infarction / Zhou, Shujing; Wang, Longbin; Huang, Xufeng; Wang, Ting; Tang, Yidan; Liu, Ying; Xu, Ming
Dátum:2024
ISSN:1945-4589
Megjegyzések:BACKGROUND: Globally, Acute Myocardial Infarction (AMI) is a common cause of heart failure (HF), which has been a leading cause of mortality resulting from non-communicable diseases. On the other hand, increasing evidence suggests that the role of energy production within the mitochondria strongly links to the development and progression of heart diseases, while Cuproptosis, a newly identified cell death mechanism, has not yet been comprehensively analyzed from the aspect of cardiovascular medicine. MATERIALS AND METHODS: 8 transcriptome profiles curated from the GEO database were integrated, from which a diagnostic model based on the Stacking algorithm was established. The efficacy of the model was evaluated in a multifaced manner (i.e., by Precision-Recall curve, Receiver Operative Characteristic curve, etc.). We also sequenced our animal models at the bulk RNA level and conducted qPCR and immunohistochemical staining, with which we further validated the expression of the key contributor gene to the model. Finally, we explored the immune implications of the key contributor gene. RESULTS: A merged machine learning model containing 4 Cuproptosis-related genes (i.e., PDHB, CDKN2A, GLS, and SLC31A1) for robust AMI diagnosis was developed, in which SLC31A1 served as the key contributor. Through in vivo modeling, we validated the aberrant overexpression of SLC31A1 in AMI. Besides, further transcriptome analysis revealed that its high expression was correlated with significant potential immunological implications in the infiltration of many immune cell types, especially monocyte. CONCLUSIONS: We constructed an AMI diagnostic model based on Cuproptosis-related genes and validated the key contributor gene in animal modeling. We also analyzed the effects on the immune system for its overexpression in AMI.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
acute myocardial infarction
bioinformatics
cuproptosis
diagnostic biomarkers
machine learning
Megjelenés:Aging. - 15 : 9 (2024), p. 1-17. -
További szerzők:Wang, Longbin Huang, Xufeng (1997-) (Bioinformatics, Oncology, Dentistry) Wang, Ting Tang, Yidan Liu, Ying Xu, Ming
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