Integrative transcriptome analysis identifies MYBL2 as a poor prognosis marker for osteosarcoma and a pan-cancer marker of immune infiltration

Qiu, Xinzhu; He, Hongbo; Zeng, Hao; Tong, Xiaopeng; Zhang, Can; Liu, Yupeng; Liao, Zhongxing; Liu, Qing

doi:10.1016/j.gendis.2023.04.035

Cited by 3 publications

(2 citation statements)

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“…Elevated expression of MYBL2 in various tumors is often associated with poor prognosis ( 45 , 46 ), rendering it a potential therapeutic target in cancer treatment. As a prognostic indicator of unfavorable outcomes in osteosarcoma and a universal marker for immune infiltration across various cancers, MYBL2 exerts regulatory control over proliferation, tumor advancement, and immune cell infiltration within osteosarcoma and broader cancer contexts ( 47 ). In clear cell renal carcinoma, MYBL2 promotes malignant characteristics and impedes apoptosis through activation of the hedgehog signaling pathway ( 48 ).…”

Section: Discussionmentioning

confidence: 99%

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma

Wei,

Lin,

Huang

et al. 2024

Front. Immunol.

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BackgroundUterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data.MethodsA comprehensive analysis was conducted using transcriptomic and clinical data from 579 endometrial cancer patients sourced from the Cancer Genome Atlas (TCGA). A subset of 503 CSGs was assessed through weighted gene co-expression network analysis (WGCNA) alongside machine learning algorithms, including Gaussian Mixture Model (GMM), support vector machine - recursive feature elimination (SVM-RFE), Random Forest, and eXtreme Gradient Boosting (XGBoost), to identify key differentially expressed cellular senescence genes. These genes underwent further analysis to construct a prognostic model.ResultsOur analysis revealed two distinct molecular clusters of UCEC with significant differences in tumor microenvironment and survival outcomes. Utilizing cellular senescence genes, a prognostic model effectively stratified patients into high-risk and low-risk categories. Patients in the high-risk group exhibited compromised overall survival and presented distinct molecular and immune profiles indicative of tumor progression. Crucially, the prognostic model demonstrated robust predictive performance and underwent validation in an independent patient cohort.ConclusionThe study emphasized the significance of cellular senescence genes in UCEC progression and underscored the efficacy of machine learning in developing reliable prognostic models. Our findings suggested that targeting cellular senescence holds promise as a strategy in personalized UCEC treatment, thus warranting further clinical investigation.

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Section: Discussionmentioning

confidence: 99%

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma

Wei,

Lin,

Huang

et al. 2024

Front. Immunol.

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“…Elevated expression of MYBL2 in various tumors is often associated with poor prognosis (45,46), rendering it a potential therapeutic target in cancer treatment. As a prognostic indicator of unfavorable outcomes in osteosarcoma and a universal marker for immune infiltration across various cancers, MYBL2 exerts regulatory control over proliferation, tumor advancement, and immune cell infiltration within osteosarcoma and broader cancer contexts (47). In clear cell renal carcinoma, MYBL2 promotes malignant characteristics and impedes apoptosis through activation of the hedgehog signaling pathway (48).…”

Section: Discussionmentioning

confidence: 99%

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Global research trends and hotspots in metabolomics of osteosarcoma: a decade-spanning bibliometric and visualized analysis

Tu,

Liu,

et al. 2024

Front. Immunol.

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ObjectOsteosarcoma is a malignant tumor originating from the bones, commonly found in children and adolescents, especially in rapidly growing bone areas such as the knees and upper arms. In this study, we aim to delineate the evolution and convergence of research themes in osteosarcoma metabolomics over the past decade, identify major contributors, and forecast emerging trends that could direct future research efforts.MethodThe bibliometric method has been applied to systematically analyze the literature in the field of osteosarcoma metabolomics. The relevant literatures were collected from the Web of Science Core Collection, spanning from January 1, 2014, to December 31, 2023. Tools such as CiteSpace, Bibliometrix, and VOSviewer were used for the visual analysis of the collected literatures. The focused information includes institutions, journals, countries, authors, keywords, and citations.ResultVarious aspects in the field of osteosarcoma metabolism were analyzed. Shanghai Jiao Tong University has published the most papers in the past ten years, followed by Central South University and Zhejiang University. Among the sources, the international journal of molecular sciences publishes the most articles, and oncotarget is the journal with the highest H index. According to Bradford’s law, there are 34 core journals identified. A total of 5501 authors participated in the creation of papers in this field. The distribution of authors follows Lotka`s Law, and 85.3% of authors have only one article. 46% of the corresponding authors are from China, but most of these corresponding authors are not good at international cooperation. China also has the largest number of publications, followed by the United States. It can be confirmed that China dominates this field. Among the keywords, “expression” is the keyword that has received the most attention in the past ten years. All keywords can be divided into 9 clusters. Based on the explosive words and hot topics each year, we speculate that future research will focus on the tumor microenvironment, molecular mechanisms and autophagy, targeted therapies and inhibitors.ConclusionIn summary, this study comprehensively analyzed the current state of research in the field of osteosarcoma metabolism through bibliometric methods. The findings revealed the development trends and research hotspots in this field, which may provide valuable references for future research directions.

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Integrative transcriptome analysis identifies MYBL2 as a poor prognosis marker for osteosarcoma and a pan-cancer marker of immune infiltration

Cited by 3 publications

References 45 publications

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma

DataSheet_2.csv

Global research trends and hotspots in metabolomics of osteosarcoma: a decade-spanning bibliometric and visualized analysis

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