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      Drug Design, Development and Therapy (submit here)

      This international, peer-reviewed Open Access journal by Dove Medical Press focuses on the design and development of drugs, as well as the clinical outcomes, patient safety, and programs targeted at the effective and safe use of medicines. Sign up for email alerts here.

      88,007 Monthly downloads/views I 4.319 Impact Factor I 6.6 CiteScore I 1.12 Source Normalized Impact per Paper (SNIP) I 0.784 Scimago Journal & Country Rank (SJR)

       

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      THSD7B Mutation Induces Platinum Resistance in Small Cell Lung Cancer Patients

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          Abstract

          Aim

          Several cases of small cell lung cancer (SCLC) patients demonstrate resistance to the treatment initiatives such as cisplatin after platinum chemotherapy. It is crucial to the improvement of the overall survival (OS) of SCLC patients to discover the gene mutation inducing platinum resistance within this cohort.

          Patients and Methods

          We analyzed the gene mutations significantly associated with the OS from 2 cohorts of SCLC platinum-treated patients. And then we screened out THSD7B mutation. In order to understand the mechanism between THSD7B mutation and platinum resistance, we designed gene mutation co-occurrence and mutual exclusivity analysis, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) analysis, and Connectivity Map (CMap) analysis.

          Results

          The poor prognosis of THSD7B mutant patients may be related to the inhibition of cell death-related pathways, the up-regulation of cell invasion and metastasis pathways, and the down-regulation of immune response pathways. Lovastatin and cyclooxygenase inhibitors could be used as potential target compounds in THSD7B mutant patients, which provides reference for future research on platinum resistance.

          Conclusion

          THSD7B can be considered a reliable biomarker that effectively facilitates the prediction of poor survival in SCLC platinum-treated patients.

          Most cited references50

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
            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

              Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)
              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.
              Article 0 comments Cited 9447 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Drug Des Devel Ther
                Journal ID (iso-abbrev): Drug Des Devel Ther
                Journal ID (publisher-id): dddt
                Title: Drug Design, Development and Therapy
                Publisher: Dove
                ISSN (Electronic): 1177-8881
                Publication date (Electronic): 03 June 2022
                Publication date Collection: 2022
                Volume: 16
                Pages: 1679-1695
                Affiliations
                [1 ]Department of Oncology, Zhujiang Hospital, Southern Medical University , Guangzhou, Guangdong, 510282, People’s Republic of China
                [2 ]The First Clinical Medical School, Nanfang Hospital, Southern Medical University , Guangzhou, Guangdong, 510515, People’s Republic of China
                Author notes
                Correspondence: Jian Zhang; Peng Luo, Email zhangjian@i.smu.edu.cn; luopeng@smu.edu.cn
                [*]

                These authors contributed equally to this work

                Author information
                https://orcid.org/http://orcid.org/0000-0002-8215-2045
                Article
                Publisher ID: 363665
                DOI: 10.2147/DDDT.S363665
                PMC ID: 9172928
                PubMed ID: 35685767
                SO-VID: 5d9a6b96-ff0f-4d2d-aae2-3837ee95a007
                Copyright statement: © 2022 Yao et al.
                License:

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                Date received : 26 February 2022
                Date accepted : 07 May 2022
                Page count
                Figures: 0, References: 51, Pages: 17
                Funding
                Funded by: There is no funding to report;
                This work was supported by the Natural Science Foundation of Guangdong Province (Grant No. 2018A030313846 and 2021A1515012593), the Science and Technology Planning Project of Guangdong Province (Grant No. 2019A030317020), Guangdong Undergraduate Training Program on Innovation and Entrepreneurship (Grant No. S202212121072) and the National Natural Science Foundation of China (Grant No. 81802257, 81871859, 81772457, 82172750 and 82172811).
                Categories
                Subject: Original Research

                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: small cell lung cancer,gene mutation,platinum,chemotherapy resistance,prognosis
                Data availability:
                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: small cell lung cancer, gene mutation, platinum, chemotherapy resistance, prognosis

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