<em>De novo</em> assembly and transcriptome characterization: Novel insights into the mechanisms of primary ovarian cancer in <em>Microtus fortis</em>

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doi:10.3892/mmr.2021.12580

De novo assembly and transcriptome characterization: Novel insights into the mechanisms of primary ovarian cancer in Microtus fortis

Authors:
- Qi Hu
- Mingyue Gao
- Du Zhang
- Bingfeng Leng
- Junwen Wang
- Qian Liu
- Shuangyan He
- Wenling Zhi
- Zhijun Zhou
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Affiliations: Department of Laboratory Animal Science, Xiangya Medical College, Central South University, Changsha, Hunan 410013, P.R. China, Department of Bioinformatics Center, NEOMICS Institute, Shenzhen, Guangdong 518118, P.R. China
Published online on: December 27, 2021 https://doi.org/10.3892/mmr.2021.12580
Article Number: 64
Copyright: © Hu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The natural incidence of primary epithelial ovarian cancer (OVC) in adult female voles of some established strains of Microtus fortis is relatively high. M. fortis OVC has some pathological similarities to human epithelial OVC, therefore M. fortis represents the latest and most valuable animal model for studying human OVC. The lack of available genetic information for M. fortis limits the use of common immunological methods; thus, high‑throughput sequencing technologies have been used to reveal the mechanisms of primary OVC in M. fortis. The individuals with cancer were diagnosed using histopathologic hematoxylin and eosin staining. The present study used RNA‑sequencing (RNA‑seq) technology to establish a de novo assembly of the M. fortis transcriptome produced 339,830 unigenes by the short reads assembly program Trinity. Comparisons were made between OVC and healthy ovarian tissue (OV) and between fallopian tube cancer (FTC) and healthy fallopian tube (FT) tissues using RNA‑seq analysis. A total of 3,434 differentially expressed genes (DEGs) were identified in OVC tissue compared with OV tissue using RNA‑Seq by Expectation‑Maximization software, including 1,950 significantly upregulated and 1,484 significantly downregulated genes. There were 2,817 DEGs identified in the FTC tissues compared with the FT tissue, including 1,762 significantly upregulated and 1,055 significantly downregulated genes. Pathway enrichment analysis revealed that upregulated transcripts in the OVC vs. OV groups were involved in cell growth and proliferation‑associated pathways, whereas the downregulated DEGS in the OVC vs. OV groups were enriched in steroid biosynthesis‑related pathways. Furthermore, the tumor suppressor gene, p53, was downregulated in the FTC and OVC compared with the FT and OV groups, respectively; whereas, genes that promoted cell migration, such as Ras‑related protein Rap‑1b, Ras homolog family member A and RAC1, were upregulated. In summary, to the best of our knowledge, the present study characterized the M. fortis de novo transcriptome of OV and FT tissues and to perform RNA‑seq quantification to analyze the differences in healthy and cancerous OV and FT tissues. These results identified pathways that differed between cancerous and healthy M. fortis tissues. Analysis of these pathways may help to reveal the pathogenesis of primary OVC in M. fortis in future work.

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1	Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI
2	Ishizuka B: Current Understanding of the etiology, symptomatology, and treatment options in premature ovarian insufficiency (POI). Front Endocrinol (Lausanne). 12:6269242021. View Article : Google Scholar : PubMed/NCBI
3	Chen SN, Chang R, Lin LT, Chern CU, Tsai HW, Wen ZH, Li YH, Li CJ and Tsui KH: MicroRNA in ovarian cancer: Biology, pathogenesis, and therapeutic opportunities. Int J Environ Res Public Health. 16:15102019. View Article : Google Scholar : PubMed/NCBI
4	Stewart C, Ralyea C and Lockwood S: Ovarian cancer: An integrated review. Semin Oncol Nurs. 35:151–156. 2019. View Article : Google Scholar : PubMed/NCBI
5	Lutgendorf SK and Sood AK: Biobehavioral factors and cancer progression: Physiological pathways and mechanisms. Psychosom Med. 73:724–730. 2011. View Article : Google Scholar : PubMed/NCBI
6	Bigorie V, Morice P, Duvillard P, Antoine M, Cortez A, Flejou JF, Uzan S, Darai E and Barranger E: Ovarian metastases from breast cancer: Report of 29 cases. Cancer. 116:799–804. 2010. View Article : Google Scholar : PubMed/NCBI
7	Mikula-Pietrasik J, Uruski P, Tykarski A and Książek K: The peritoneal ‘soil’ for a cancerous ‘seed’: A comprehensive review of the pathogenesis of intraperitoneal cancer metastases. Cell Mol Life Sci. 75:509–525. 2018. View Article : Google Scholar : PubMed/NCBI
8	Parashar D, Nair B, Geethadevi A, George J, Nair A, Tsaih SW, Kadamberi IP, Gopinadhan Nair GK, Lu Y, Ramchandran R, et al: Peritoneal spread of ovarian cancer harbors therapeutic vulnerabilities regulated by FOXM1 and EGFR/ERBB2 signaling. Cancer Res. 80:5554–5568. 2020. View Article : Google Scholar : PubMed/NCBI
9	Levy A, Medjhoul A, Caramella C, Zareski E, Berges O, Chargari C, Boulet B, Bidault F, Dromain C and Balleyguier C: Interest of diffusion-weighted echo-planar MR imaging and apparent diffusion coefficient mapping in gynecological malignancies: A review. J Magn Reson Imaging. 33:1020–1027. 2011. View Article : Google Scholar : PubMed/NCBI
10	Li Z, Zheng W, Wang H, Cheng Y, Fang Y, Wu F, Sun G, Sun G, Lv C and Hui B: Application of animal models in cancer research: Recent progress and future prospects. Cancer Manag Res. 13:2455–2475. 2021. View Article : Google Scholar : PubMed/NCBI
11	Overgaard NH, Fan TM, Schachtschneider KM, Principe DR, Schook LB and Jungersen G: Of Mice, Dogs, Pigs, and Men: Choosing the appropriate model for immuno-oncology research. ILAR J. 59:247–262. 2018. View Article : Google Scholar : PubMed/NCBI
12	Jayson GC, Kohn EC, Kitchener HC and Ledermann JA: Ovarian cancer. Lancet. 384:1376–1388. 2014. View Article : Google Scholar : PubMed/NCBI
13	Katt ME, Placone AL, Wong AD, Xu ZS and Searson PC: In Vitro tumor models: Advantages, disadvantages, variables, and selecting the right platform. Front Bioeng Biotechnol. 4:122016. View Article : Google Scholar : PubMed/NCBI
14	Kamdem JP, Duarte AE, Ibrahim M, Lukong KE, Barros LM and Roeder T: Bibliometric analysis of personalized humanized mouse and Drosophila models for effective combinational therapy in cancer patients. Biochim Biophys Acta Mol Basis Dis. 1866:1658802020. View Article : Google Scholar : PubMed/NCBI
15	Yu YJ, Su ZJ, Zhou ZJ and Ma YD: Pathological observation on spontaneous epithelial ovarian cancer in reed vole. Chin J V Sci. 1070–1073. 2008.(In Chinese).
16	Zhou ZJ, Yu YJ, Su ZJ and Tang LF: Expression of heat shock proteins HSP27, HSP70 and HSP90 in spontaneous ovarian cancer of Microtus fortis. Chin J Comp Med. 4:26–28. 2005.(In Chinese).
17	Yu Y: An investigation of pathological changes in Captive-bred Microtus fortis. Chin J Zool. 38:71–73. 2003.(In Chinese).
18	Zheng M, Hu Y, Gou R, Nie X, Li X, Liu J and Lin B: Identification three LncRNA prognostic signature of ovarian cancer based on genome-wide copy number variation. Biomed Pharmacother. 124:1098102020. View Article : Google Scholar : PubMed/NCBI
19	Hu Y, Xu Y, Lu W, Yuan Z, Quan H, Shen Y and Cao J: De novo assembly and transcriptome characterization: Novel insights into the natural resistance mechanisms of Microtus fortis against Schistosoma japonicum. BMC Genomics. 15:4172014. View Article : Google Scholar : PubMed/NCBI
20	Marquardt N, Feja M, Hünigen H, Plendl J, Menken L, Fink H and Bert B: Euthanasia of laboratory mice: Are isoflurane and sevoflurane real alternatives to carbon dioxide? PLoS One. 13:e02037932018. View Article : Google Scholar : PubMed/NCBI
21	Laferriere CA, Leung VS and Pang DS: Evaluating intrahepatic and intraperitoneal sodium pentobarbital or ethanol for mouse euthanasia. J Am Assoc Lab Anim Sci. 59:264–268. 2020. View Article : Google Scholar : PubMed/NCBI
22	Bolger AM, Lohse M and Usadel B: Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 30:2114–2120. 2014. View Article : Google Scholar : PubMed/NCBI
23	Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 29:644–652. 2011. View Article : Google Scholar : PubMed/NCBI
24	R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Computing,. 14:pp12–21. 2009.
25	Eddy SR: Profile hidden markov models. Bioinformatics. 14:755–763. 1998. View Article : Google Scholar : PubMed/NCBI
26	Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, von Heijne G and Nielsen H: SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 37:420–423. 2019. View Article : Google Scholar : PubMed/NCBI
27	Love MI, Huber W and Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:5502014. View Article : Google Scholar : PubMed/NCBI
28	Langmead B and Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods. 9:357–359. 2012. View Article : Google Scholar : PubMed/NCBI
29	Li B and Dewey CN: RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 12:3232011. View Article : Google Scholar : PubMed/NCBI
30	Leung CS, Yeung TL, Yip KP, Pradeep S, Balasubramanian L, Liu J, Wong KK, Mangala LS, Armaiz-Pena GN, Lopez-Berestein G, et al: Calcium-dependent FAK/CREB/TNNC1 signalling mediates the effect of stromal MFAP5 on ovarian cancer metastatic potential. Nat Commun. 5:50922014. View Article : Google Scholar : PubMed/NCBI
31	Takahashi Y, Li L, Kamiryo M, Asteriou T, Moustakas A, Yamashita H and Heldin P: Hyaluronan fragments induce endothelial cell differentiation in a CD44- and CXCL1/GRO1-dependent manner. J Biol Chem. 280:24195–24204. 2005. View Article : Google Scholar : PubMed/NCBI
32	Onaciu A, Munteanu R, Munteanu VC, Gulei D, Raduly L, Feder RI, Pirlog R, Atanasov AG, Korban SS, Irimie A and Berindan-Neagoe I: Spontaneous and induced animal models for cancer research. Diagnostics (Basel). 10:6602020. View Article : Google Scholar : PubMed/NCBI
33	Cicalese A, Bonizzi G, Pasi CE, Faretta M, Ronzoni S, Giulini B, Brisken C, Minucci S, Di Fiore PP and Pelicci PG: The tumor suppressor p53 regulates polarity of self-renewing divisions in mammary stem cells. Cell. 138:1083–1095. 2009. View Article : Google Scholar : PubMed/NCBI
34	Yue XT, Zhao Y, Xu Y, Zheng M, Feng Z and Hu W: Mutant p53 in Cancer: Accumulation, Gain-of-function, and therapy. J Mol Biol. 429:1595–1606. 2017. View Article : Google Scholar : PubMed/NCBI
35	Barkal AA, Brewer RE, Markovic M, Kowarsky M, Barkal SA, Zaro BW, Krishnan V, Hatakeyama J, Dorigo O, Barkal LJ and Weissman IL: CD24 signalling through macrophage Siglec-10 is a target for cancer immunotherapy. Nature. 572:392–396. 2019. View Article : Google Scholar : PubMed/NCBI
36	Lee KB, Byun HJ, Park SH, Park CY, Lee SH and Rho SB: CYR61 controls p53 and NF-κB expression through PI3K/Akt/mTOR pathways in carboplatin-induced ovarian cancer cells. Cancer Lett. 315:86–95. 2012. View Article : Google Scholar : PubMed/NCBI
37	von Karstedt S, Conti A, Nobis M, Montinaro A, Hartwig T, Lemke J, Legler K, Annewanter F, Campbell AD, Taraborrelli L, et al: Cancer cell-autonomous TRAIL-R signaling promotes KRAS-driven cancer progression, invasion, and metastasis. Cancer Cell. 27:561–573. 2015. View Article : Google Scholar : PubMed/NCBI
38	Lin KT, Yeh YM, Chuang CM, Yang SY, Chang JW, Sun SP, Wang YS, Chao KC and Wang LH: Glucocorticoids mediate induction of microRNA-708 to suppress ovarian cancer metastasis through targeting Rap1B. Nat Commun. 6:59172015. View Article : Google Scholar : PubMed/NCBI
39	Leng R, Liao G, Wang H, Kuang J and Tang L: Rac1 expression in epithelial ovarian cancer: Effect on cell EMT and clinical outcome. Med Oncol. 32:3292015. View Article : Google Scholar : PubMed/NCBI
40	Ren XL, Zhu XH, Li XM, Li YL, Wang JM, Wu PX, Lv ZB, Ma WH, Liao WT, Wang W, et al: Down-regulation of BTG3 promotes cell proliferation, migration and invasion and predicts survival in gastric cancer. J Cancer Res Clin Oncol. 141:397–405. 2015. View Article : Google Scholar : PubMed/NCBI
41	Mao D, Qiao L, Lu H and Feng Y: B-cell translocation gene 3 overexpression inhibits proliferation and invasion of colorectal cancer SW480 cells via Wnt/β-catenin signaling pathway. Neoplasma. 63:705–716. 2016. View Article : Google Scholar : PubMed/NCBI
42	Deng B, Zhao Y, Gou W, Chen S, Mao X, Takano Y and Zheng H: Decreased expression of BTG3 was linked to carcinogenesis, aggressiveness, and prognosis of ovarian carcinoma. Tumor Biol. 34:2617–2624. 2013. View Article : Google Scholar : PubMed/NCBI
43	Bai Y, Qiao L, Xie N, Shi Y, Liu N and Wang J: Expression and prognosis analyses of the Tob/BTG antiproliferative (APRO) protein family in human cancers. PLoS One. 12:e01849022017. View Article : Google Scholar : PubMed/NCBI
44	Liu T, Yang Y, Xie Z, Luo Q, Yang D, Liu X, Zhao H, Wei Q, Liu Y, Li L, et al: The RNA binding protein QKI5 suppresses ovarian cancer via downregulating transcriptional coactivator TAZ. Mol Ther Nucleic Acids. 26:388–400. 2021. View Article : Google Scholar : PubMed/NCBI
45	Cui G, Wang C, Lin Z, Feng X, Wei M, Miao Z, Sun Z and Wei F: Prognostic and immunological role of Ras-related protein Rap1b in pan-cancer. Bioengineered. 12:4828–4840. 2021. View Article : Google Scholar : PubMed/NCBI
46	Wei X, Lou H, Zhou D, Jia Y, Li H, Huang Q, Ma J, Yang Z, Sun C, Meng Y, et al: TAGLN mediated stiffness-regulated ovarian cancer progression via RhoA/ROCK pathway. J Exp Clin Cancer Res. 40:2922021. View Article : Google Scholar : PubMed/NCBI
47	Wang X, Wei Z, Tang Z, Xue C, Yu H, Zhang D, Li Y, Liu X, Shi Y, Zhang L, et al: IL-37bΔ1-45 suppresses the migration and invasion of endometrial cancer cells by targeting the Rac1/NF-κB/MMP2 signal pathway. Lab Invest. 101:760–774. 2021. View Article : Google Scholar : PubMed/NCBI
48	Yanagida S, Taniue K, Sugimasa H, Nasu E, Takeda Y, Kobayashi M, Yamamoto T, Okamoto A and Akiyama T: ASBEL, an ANA/BTG3 antisense transcript required for tumorigenicity of ovarian carcinoma. Sci Rep. 3:13052013. View Article : Google Scholar : PubMed/NCBI