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EZR-ROS1 fusion renal cell carcinoma mimicking urothelial carcinoma: report of a previously undescribed gene fusion in renal cell carcinoma. Virchows Arch 2021; 480:487-492. [PMID: 34128116 DOI: 10.1007/s00428-021-03138-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022]
Abstract
A subset of renal cell carcinomas harbor gene fusions, and we report the first case of an EZR-ROS1 fusion in renal cell carcinoma. A 47-year-old female presented with hematuria and a mass involving the renal pelvis. Renal biopsy revealed a tumor with solid and tubular architecture that was diffusely positive for PAX8, CK7, and vimentin; retained expression of INI1; focally positive for P504S; and negative for GATA3 and TFE3. Partial nephrectomy was performed, and histologically, the tumor had solid and tubular architecture with mucin-like content within tubules. The nuclei corresponded to WHO/ISUP grade 2 and 3. The morphology was neither specific nor diagnostic, and a final diagnosis of unclassified renal cell carcinoma with solid and tubular architecture was rendered. Molecular sequencing revealed an EZR-ROS1 fusion: a fusion that has never been reported in renal cell carcinoma. Recognition of this fusion is of significance if the tumor becomes metastatic as treatment with crizotinib may be considered.
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Panagopoulos I, Heim S. Interstitial Deletions Generating Fusion Genes. Cancer Genomics Proteomics 2021; 18:167-196. [PMID: 33893073 DOI: 10.21873/cgp.20251] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 12/16/2022] Open
Abstract
A fusion gene is the physical juxtaposition of two different genes resulting in a structure consisting of the head of one gene and the tail of the other. Gene fusion is often a primary neoplasia-inducing event in leukemias, lymphomas, solid malignancies as well as benign tumors. Knowledge about fusion genes is crucial not only for our understanding of tumorigenesis, but also for the diagnosis, prognostication, and treatment of cancer. Balanced chromosomal rearrangements, in particular translocations and inversions, are the most frequent genetic events leading to the generation of fusion genes. In the present review, we summarize the existing knowledge on chromosome deletions as a mechanism for fusion gene formation. Such deletions are mostly submicroscopic and, hence, not detected by cytogenetic analyses but by array comparative genome hybridization (aCGH) and/or high throughput sequencing (HTS). They are found across the genome in a variety of neoplasias. As tumors are increasingly analyzed using aCGH and HTS, it is likely that more interstitial deletions giving rise to fusion genes will be found, significantly impacting our understanding and treatment of cancer.
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Affiliation(s)
- Ioannis Panagopoulos
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway;
| | - Sverre Heim
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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He SY, Xi WJ, Wang X, Xu CH, Cheng L, Liu SY, Meng QQ, Li B, Wang Y, Shi HB, Wang HJ, Wang ZZ. Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung. Med Sci Monit 2019; 25:3941-3956. [PMID: 31132294 PMCID: PMC6556069 DOI: 10.12659/msm.913727] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Background Adenocarcinoma of the lung is a type of non-small cell lung cancer (NSCLC). Clinical outcome is associated with tumor grade, stage, and subtype. This study aimed to identify RNA expression profiles, including long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA, associated with clinical outcome in adenocarcinoma of the lung using bioinformatics data. Material/Methods The miRNA and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and lncRNA expression profiles were downloaded from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. The independent dataset, the Gene Expression Omnibus (GEO) accession dataset, GSE81089, was used. RNA expression profiles were used to identify comprehensive prognostic RNA signatures based on patient survival time. Results From 7,704 lncRNAs, 787 miRNAs, and 28,937 mRNAs of 449 patients, four joint RNA molecular signatures were identified, including RP11-909N17.2, RP11-14N7.2 (lncRNAs), MIR139 (miRNA), KLHDC8B (mRNA). The random forest (RF) classifier was used to test the prediction ability of patient survival risk and showed a good predictive accuracy of 71% and also showed a significant difference in overall survival (log-rank P=0.0002; HR, 3.54; 95% CI, 1.74–7.19). The combined RNA signature also showed good performance in the identification of patient survival in the validation and independent datasets. Conclusions This study identified four RNA sequences as a prognostic molecular signature in adenocarcinoma of the lung, which may also provide an increased understanding of the molecular mechanisms underlying the pathogenesis of this malignancy.
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Affiliation(s)
- Si-Yu He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Wen-Jing Xi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Xin Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Chao-Han Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Si-Yao Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Qian-Qian Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Boyan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Hong-Bo Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Hong-Jiu Wang
- College of Science, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China (mainland)
| | - Zhen-Zhen Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
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