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      Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

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          Abstract

          Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein–protein interaction network, some promising target candidates ( GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.

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          Most cited references85

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          Diagnosis and Treatment of Pituitary Adenomas

          Pituitary adenomas may hypersecrete hormones or cause mass effects. Therefore, early diagnosis and treatment are important.
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            The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.

            The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.
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              NOREVA: normalization and evaluation of MS-based metabolomics data

              Abstract Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                03 January 2019
                January 2019
                : 20
                : 1
                : 151
                Affiliations
                [1 ]School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China; yangqx@ 123456cqu.edu.cn (Q.Y.); 20132902008@ 123456cqu.edu.cn (J.T.); libcell@ 123456cqu.edu.cn (B.L.)
                [2 ]College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; lfwyx@ 123456zju.edu.cn (Y.W.); zhangsong_@ 123456zju.edu.cn (S.Z.); lifengcheng@ 123456zju.edu.cn (F.L.); yinjiayi@ 123456zju.edu.cn (J.Y.); liyi@ 123456email.com (Y.L.); fujianbo@ 123456zju.edu.cn (J.F.); luo.yongchao@ 123456foxmail.com (Y.L.)
                Author notes
                [* ]Correspondence: xueww@ 123456cqu.edu.cn (W.X.); zhufeng@ 123456zju.edu.cn (F.Z.); Tel.: +86-(0)571-8820-8444 (W.X. & F.Z.)
                Author information
                https://orcid.org/0000-0002-3285-0574
                https://orcid.org/0000-0001-8069-0053
                Article
                ijms-20-00151
                10.3390/ijms20010151
                6337483
                30609812
                3e8a577b-3e57-4c7f-8731-e631f892f272
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 09 December 2018
                : 26 December 2018
                Categories
                Article

                Molecular biology
                pituitary adenomas,immunotherapy,immune-related gene markers,transcriptomics
                Molecular biology
                pituitary adenomas, immunotherapy, immune-related gene markers, transcriptomics

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