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      Key sunitinib‐related biomarkers for renal cell carcinoma

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          Abstract

          Background

          Renal cell carcinoma (RCC) contributed to 403,262 new cases worldwide in 2018, which constitutes 2.2% of global cancer, nevertheless, sunitinib, one of the major targeted therapeutic agent for RCC, often developed invalid due to resistance. Emerging evidences suggested sunitinib can impact tumor environment which has been proven to be a vital factor for tumor progression.

          Methods

          In the present study, we used ssGSEA to extract the immune infiltrating abundance of clear cell RCC (ccRCC) and normal control samples from GSE65615, TCGA, and GTEx; key immune cells were determined by Student's t‐test and univariable Cox analysis. Co‐expression network combined with differentially expressed analysis was then applied to derive key immune‐related genes for ccRCC, followed by the identification of hub genes using differential expression analysis. Subsequently, explorations and validations of the biological function and the immune‐related and sunitinib‐related characteristics were conducted in KEGG, TISIDB, Oncomine, ICGC, and GEO databases.

          Results

          We refined immature dendritic cells and central memory CD4 T cells which showed associations with sunitinib and ccRCC. Following, five hub genes (CRYBB1, RIMBP3C, CEACAM4, HAMP, and LYL1) were identified for their strong relationships with sunitinib and immune infiltration in ccRCC. Further validations in external data refined CRYBB1, CEACAM4, and HAMP which play a vital role in sunitinib resistance, immune infiltrations in ccRCC, and the development and progression of ccRCC. In conclusion, our findings could shed light on the resistance of sunitinib in ccRCC and provide novel biomarkers or drug targets for ccRCC.

          Abstract

          We identified five hub genes (CRYBB1, RIMBP3C, CEACAM4, HAMP, and LYL1) related to immune infiltration, sunitinib in renal carcinoma. External validation refined CRYBB1, CEACAM4 and HAMP which played a vital role in the development and progression of ccRCC and implicated in sunitinib resistance and immune infiltrations in ccRCC.

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          Most cited references62

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          Ahmedin Jemal, Lindsey A Torre, Rebecca Siegel … (2018)
          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            Michael Love, Wolfgang Huber, Simon Anders (2014)
            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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                Author and article information

                Contributors
                Haitao Wang:
                ORCID: https://orcid.org/0000-0003-4345-9375
                haitao_peterrock@outlook.com
                Journal
                Journal ID (nlm-ta): Cancer Med
                Journal ID (iso-abbrev): Cancer Med
                Journal ID (doi): 10.1002/(ISSN)2045-7634
                Journal ID (publisher-id): CAM4
                Title: Cancer Medicine
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 2045-7634
                Publication date (Electronic): 17 August 2021
                Publication date Collection: October 2021
                Volume: 10
                Issue: 19 ( doiID: 10.1002/cam4.v10.19 )
                Pages: 6917-6930
                Affiliations
                [ 1 ] Tianjin Institute of Urology The 2nd Hospital of Tianjin Medical University Tianjin China
                [ 2 ] Department of Urology Tianjin Medical University General Hospital Tianjin China
                [ 3 ] Department of Oncology The 2nd Hospital of Tianjin Medical University Tianjin China
                Author notes
                [*] [* ] Correspondence

                Haitao Wang, Department of Oncology, The 2nd Hospital of Tianjin Medical University, Tianjin 300211, China.

                Email: haitao_peterrock@ 123456outlook.com

                Author information
                https://orcid.org/0000-0003-4345-9375
                Article
                Publisher ID: CAM44206
                DOI: 10.1002/cam4.4206
                PMC ID: 8495283
                PubMed ID: 34402193
                SO-VID: 23c56775-7c93-42b1-b16a-22ac7eb24db5
                Copyright © © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Date revision received : 21 July 2021
                Date received : 07 May 2021
                Date accepted : 28 July 2021
                Page count
                Figures: 11, Tables: 0, Pages: 14, Words: 7047
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81572543
                Funded by: Science and Technology Support Program of Tianjin
                Award ID: 17ZXMFSY00040
                Funded by: Clinical Research of Tianjin Medical University
                Award ID: 2018kylc004
                Categories
                Subject: Research Article
                Subject: Bioinformatics
                Subject: Research Articles
                Custom metadata
                source-schema-version-number 2.0
                cover-date October 2021
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.8 mode:remove_FC converted:07.10.2021

                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: ceacam4,crybb1,hamp,renal cell carcinoma,sunitinib
                Data availability:
                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: ceacam4, crybb1, hamp, renal cell carcinoma, sunitinib

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