Discovery and Classification of Fusion Transcripts in Prostate Cancer and Normal Prostate Tissue

Luo, Jianhua; Liu, Silvia; Zuo, Ze-Hua; Chen, Rui; Tseng, George C.; Yu, Yan

doi:10.1016/j.ajpath.2015.03.008

Cited by 30 publications

(50 citation statements)

References 98 publications

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“…The discovery of fusion protein transcripts in the recent times have helped studying prostate cancer development with much detail. ALG5, Dolichyl-Phosphate Beta-Glucosyltransferase and PIGU, Phosphatidylinositol Glycan Anchor Biosynthesis Class forms a chimericfusion protein transcript in which glucosyltransferase, the head from ALG5 is retained but GPI transamidase, the tail has been eliminated in PIGU resulting in the loss of functionality of both the genes [50]. The uncommon joining of the genes would result in serious complications in the overall environment of the cell causing further progression of the cancer.…”

Section: Biological Insightmentioning

confidence: 99%

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data

et al. 2020

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Background: Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor -the laterality -can be unilateral (the tumor is affecting one side of the prostate), or bilateral on both sides. Nevertheless, the tumor can be overestimated or underestimated by standard screening methods. In this work, a combination of efficient machine learning methods for feature selection and classification are proposed to analyze gene activity and select them as relevant biomarkers for different laterality samples. Results: A data set that consists of 450 samples was used in this study. The samples were divided into three laterality classes ( left, right, bilateral). The aim of this work is to understand the genomic activity in each class and find relevant genes as indicators for each class with nearly 99% accuracy. The system identified groups of differentially expressed genes (RTN1, HLA-DMB, MRI1) that are able to differentiate samples among the three classes. Conclusion: The proposed method was able to detect sets of genes that can identify different laterality classes. The resulting genes are found to be strongly correlated with disease progression. HLA-DMB and EIF4G2, which are detected in the set of genes can detect the left laterality, were reported earlier to be in the same pathway called Allograft rejection SuperPath.

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Section: Biological Insightmentioning

confidence: 99%

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data

et al. 2020

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“…Thus, early detection of HCC is crucial for reducing the mortality of liver cancer. Recently, we identified a panel of 8 fusion genes in human cancers [7][8][9]. Some of these fusion genes were shown to be present in a large proportion of HCC cancer samples [7,10].…”

Section: Introductionmentioning

confidence: 99%

“…Some of these fusion genes were shown to be present in a large proportion of HCC cancer samples [7,10]. The mechanisms underlying these gene fusions are chromosomal translocation and rearrangement [8][9][10]. The presence of these fusion transcripts in liver cancer samples indicates that translocation and chromosomal rearrangement are common in liver cancer cells.…”

Section: Introductionmentioning

confidence: 99%

Detection of fusion transcripts in the serum samples of patients with hepatocellular carcinoma

Tsung

Liu

et al. 2019

Oncotarget

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Hepatocellular carcinoma is one of the most lethal cancers in the United States. Early detection of the disease is crucial for reducing the mortality of this malignancy. Recently, we identified a panel of fusion genes present in several types of human cancers, including hepatocellular carcinoma. Among 8 fusion genes, MAN2A1-FER, TRMT11-GRIK2 and CCNH-C5orf30 appear most frequently in hepatocellular carcinoma samples. In this study, we showed that the fusion transcripts of MAN2A1-FER, CCNH-C5orf30 and SLC45A2-AMACR were detected in the serum samples of liver cancer patients as circulating cell-free RNA. The distributions of these gene fusion RNA fragments largely matched those of the primary HCC samples. In contrast, the sera of all healthy individuals free of human malignancies were shown to be negative for these fusion genes. These results suggest that gene fusion RNA is frequently shed from liver cancer cells. The detection of serum cell-free fusion transcripts may provide a new approach to aid in the diagnosis, follow-up or therapy of liver cancers.

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“…Cancer‐related genetic alterations include amplification, deletion, duplication, and chromosomal rearrangement (chromosome translocation, inversion, and deletion). These aberrations can exert potent impacts on the development or progression of cancer . Gene fusions commonly are caused by chromosomal rearrangement.…”

Section: Introductionmentioning

confidence: 99%

“…These aberrations can exert potent impacts on the development or progression of cancer. 3,4 Gene fusions commonly are caused by chromosomal rearrangement. Many gene fusions have been identified in leukemias, lymphomas, and sarcomas, with the BCR-ABL fusion gene observed in patients with chronic leukemia being a notable example.…”

Section: Introductionmentioning

confidence: 99%

The novel fusion transcript NR5A2‐KLHL29FT is generated by an insertion at the KLHL29 locus

Sun

Salzberg

et al. 2017

Cancer

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Background Novel fusion transcripts caused by chromosomal rearrangement are common factors in the development of cancers. We used massively parallel RNA-sequencing to identify new fusion transcripts in colon cancers. Methods RNA-seq and TopHat-Fusion were used to identify new fusion transcripts in colon cancers. We then investigated whether the novel fusion transcript NR5A2-KLHL29FT was transcribed from a genomic chromosomal rearrangement. Next, the expression of NR5A2-KLHL29FT was measured by quantitative RT-PCR in colon cancers and matched corresponding normal epithelia. Results We identified the fusion transcript NR5A2-KLHL29FT in normal and cancerous epithelia. While investigating this transcript, we unexpectedly found that it was due to an uncharacterized polymorphic germline insertion of NR5A2 sequence from chromosome 1 into the KLHL29 locus at chromosome 2, rather than a chromosomal rearrangement. This germline insertion, which occurred at a population frequency of 0.40, appeared to bear no relationship to cancer development. Moreover, NR5A2-KLHL29FT expression was validated in RNAs from samples with insertions of NR5A2 at the KLHL29 gene locus, but not from samples without this insertion. Notably, NR5A2-KLH29FT expression levels were significantly lower in colon cancers than in matched normal colonic epithelia (p = 0.029), suggesting potential participation of NR5A2-KLHL29FT in the origin or progression of this tumor type. Conclusions NR5A2-KLHL29FT was generated from a polymorphism insertion of NR5A2 sequence into the KLHL29 locus. NR5A2-KLHL29FT may influence the origin or progression of colon cancer. Moreover, researchers should be aware that similar fusion transcripts may occur due to trans-chromosomal insertions that are not correctly annotated in genome databases, especially with current assembly algorithms.

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Discovery and Classification of Fusion Transcripts in Prostate Cancer and Normal Prostate Tissue

Cited by 30 publications

References 98 publications

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data

Detection of fusion transcripts in the serum samples of patients with hepatocellular carcinoma

The novel fusion transcript NR5A2‐KLHL29FT is generated by an insertion at the KLHL29 locus

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