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      Identification of TIMP1 as an inflammatory biomarker associated with temporal lobe epilepsy based on integrated bioinformatics and experimental analyses

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          Abstract

          Background

          Epilepsy is the second most prevalent neurological disease. Although there are many antiseizure drugs, approximately 30% of cases are refractory to treatment. Temporal lobe epilepsy (TLE) is the most common epilepsy subtype, and previous studies have reported that hippocampal inflammation is an important mechanism associated with the occurrence and development of TLE. However, the inflammatory biomarkers associated with TLE are not well defined.

          Methods

          In our study, we merged human hippocampus datasets (GSE48350 and GSE63808) through batch correction and generally verified the diagnostic roles of inflammation-related genes (IRGs) and subtype classification according to IRGs in epilepsy through differential expression, random forest, support vector machine, nomogram, subtype classification, enrichment, protein‒protein interaction, immune cell infiltration, and immune function analyses. Finally, we detected the location and expression of inhibitor of metalloproteinase-1 (TIMP1) in epileptic patients and kainic acid-induced epileptic mice.

          Results

          According to the bioinformatics analysis, we identified TIMP1 as the most significant IRG associated with TLE, and we found that TIMP1 was mainly located in cortical neurons and scantly expressed in cortical gliocytes by immunofluorescence staining. We detected decreased expression of TIMP1 by quantitative real-time polymerase chain reaction and western blotting.

          Conclusion

          TIMP1, the most significant IRG associated with TLE, might be a novel and promising biomarker to study the mechanism of epilepsy and guide the discovery of new drugs for its treatment.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12974-023-02837-3.

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          Most cited references43

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          pROC: an open-source package for R and S+ to analyze and compare ROC curves

          Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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            ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

            Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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              Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer.

              The complex interactions between tumors and their microenvironment remain to be elucidated. Combining large-scale approaches, we examined the spatio-temporal dynamics of 28 different immune cell types (immunome) infiltrating tumors. We found that the immune infiltrate composition changed at each tumor stage and that particular cells had a major impact on survival. Densities of T follicular helper (Tfh) cells and innate cells increased, whereas most T cell densities decreased along with tumor progression. The number of B cells, which are key players in the core immune network and are associated with prolonged survival, increased at a late stage and showed a dual effect on recurrence and tumor progression. The immune control relevance was demonstrated in three endoscopic orthotopic colon-cancer mouse models. Genomic instability of the chemokine CXCL13 was a mechanism associated with Tfh and B cell infiltration. CXCL13 and IL21 were pivotal factors for the Tfh/B cell axis correlating with survival. This integrative study reveals the immune landscape in human colorectal cancer and the major hallmarks of the microenvironment associated with tumor progression and recurrence. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                1148451043@qq.com
                1039471369@qq.com
                1923396887@qq.com
                837041654@qq.com
                821641739@qq.com
                1435025601@qq.com
                1280634107@qq.com
                wenyuetao@cqu.edu.cn
                Journal
                J Neuroinflammation
                J Neuroinflammation
                Journal of Neuroinflammation
                BioMed Central (London )
                1742-2094
                26 June 2023
                26 June 2023
                2023
                : 20
                : 151
                Affiliations
                [1 ]GRID grid.190737.b, ISNI 0000 0001 0154 0904, Department of Physical Examination Center, Chongqing University Jiangjin Hospital, , Chongqing University, ; Chongqing, China
                [2 ]GRID grid.203458.8, ISNI 0000 0000 8653 0555, Department of Neurosurgery, , Yongchuan Hospital of Chongqing Medical University, ; Chongqing, China
                [3 ]GRID grid.412461.4, ISNI 0000 0004 9334 6536, Department of Neurology, , The Second Affiliated Hospital of Chongqing Medical University, ; Chongqing, China
                [4 ]GRID grid.190737.b, ISNI 0000 0001 0154 0904, Department of Neurology, Chongqing University Three Gorges Hospital, , Chongqing University, ; Chongqing, China
                [5 ]GRID grid.190737.b, ISNI 0000 0001 0154 0904, Department of Neurosurgery, Chongqing University Jiangjin Hospital, , Chongqing University, ; Chongqing, China
                Article
                2837
                10.1186/s12974-023-02837-3
                10294353
                37365625
                76eb1624-c43d-426b-8d9d-5840758c36e9
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 3 April 2023
                : 19 June 2023
                Funding
                Funded by: Natural Science Foundation of Chongqing
                Award ID: cstc2020jcyj-msxmX1005
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Neurosciences
                temporal lobe epilepsy,inflammation-related genes,bioinformatics analysis,timp1,biomarker

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