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      CD146 is closely associated with the prognosis and molecular features of osteosarcoma: Guidance for personalized clinical treatment

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          Abstract

          Background: Osteosarcoma (OSA), a focus for orthopedic surgeons, always results in severe death due to metastasis. CD146 is severely expressed in several tumors, indicating its potential as a biomarker for OSA.

          Method: Two OSA cohorts were enrolled in this study. A Therapeutically Applicable Research to Generate Effective Treatments-Osteosarcoma (TARGET-OS) cohort was used as a training cohort, and GSE21257 was used as the external validation cohort. The R package “limma” was used to discriminate the differentially expressed genes among CD146-high and CD146-low patients and was further annotated by the enriched signaling pathways. The R package MOVICS was used to evaluate immune infiltration and the response to chemotherapy and immunotherapy. All statistical analyses were performed by R version 4.0.2, and p < 0.05 was considered statistically significant.

          Result: CD146 plays an important role in promoting the progression, invasion, and metastasis of several tumors. In the current study, we first revealed an integrative unfavorable prognosis in patients with tumors ( p < 0.01, HR: 1.10, 95% CI: 1.07-1.14). CD146 is tightly correlated with m5C RNA methylation modification genes in OSA. Furthermore, we revealed that CD146 acts as an oncogene in OSA patients and is linked to poor prognosis in both the TARGET-OS cohort ( p = 0.019, HR: 2.61, 95% CI: 1.171-5.834) and the GSE21257 cohort ( p = 0.005, HR: 3.61, 95% CI: 1.474-8.855), with a total of 137 patients, regardless of whether they were adjusted for clinical pathological features. Highly-expressed CD146 impacts the signaling pathways of cytokine‒cytokine receptor interactions and is associated with the high infiltration of immunocytes. Moreover, patients with high CD146 expression were more likely to be sensitive to anti-PD-1 immunotherapy, while patients with low expression of CD146 were more likely to be sensitive to cisplatin and doxorubicin chemotherapy.

          Conclusion: Overall, CD146 is an independent prognostic factor for OSA patients and can help doctors select clinical treatment strategies.

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          Most cited references59

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                21 October 2022
                2022
                : 13
                : 1025306
                Affiliations
                Department of Orthopaedics , The Second Affiliated Hospital of Anhui Medical University , Hefei, China
                Author notes

                Edited by: Xiaofan Lu, INSERM U964 Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), France

                Reviewed by: Dan Zhang, Institute of Genetics and Developmental Biology (CAS), China

                Sun Zhaoyang, Fudan University, China

                Zhijia Xia, Ludwig Maximilian University of Munich, Germany

                *Correspondence: XinZhong Xu, xuxinzhong@ 123456ahmu.edu.cn

                This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics

                These authors have contributed equally to this work and share first authorship

                Article
                1025306
                10.3389/fgene.2022.1025306
                9635853
                36338992
                3d1b6c0f-2c2f-431c-aca4-80079eae9240
                Copyright © 2022 Wang, Wu, Zheng, Yu, Zhang and Xu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 August 2022
                : 29 September 2022
                Categories
                Genetics
                Original Research

                Genetics
                osteosarcoma,prognosis,microenvironment,immunotherapy,personalized treatment
                Genetics
                osteosarcoma, prognosis, microenvironment, immunotherapy, personalized treatment

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