Clinical significance and oncogene function of long noncoding RNA HAGLROS overexpression in ovarian cancer

  • Meiqin Yang
  • Zhensheng Zhai
  • Yunfeng Zhang
  • Yue WangEmail author
Gynecologic Oncology



To explore the clinical significance and mechanism of long noncoding RNA (lncRNA) HAGLROS in ovarian cancer.


The expression of HAGLROS in ovarian cancer was verified by online databases and quantitative reverse transcription polymerase chain reaction (qRT-PCR), and its relationship with clinicopathological parameters was analysed. Pearson correlation analysis was used to study the correlation between HAGLROS and miR-100 in ovarian cancer. Meta-analysis was used to explore the expression of miR-100 in ovarian cancer. In addition, we used bioinformatics to explore the target genes of miR-100 and perform functional analysis.


HAGLROS was significantly upregulated in ovarian cancer (P < 0.001) and was closely related to disease stage (P = 0.033), tumour size (P = 0.032) and poor prognosis (P = 0.019). HAGLROS had a certain diagnostic value in ovarian cancer (area under the curve = 0.751). MiR-100 was negatively correlated with HAGLROS (r = 0.167, P = 0.001) and significantly downregulated in ovarian cancer. Bioinformatics analysis predicted a total of 31 potential target genes that interact with miR-100. These target genes were mainly involved in the regulation of cellular catabolic process, proteoglycan biosynthetic process and positive regulation of proteasomal ubiquitin-dependent protein catabolic process. Among them, mTOR and ZNRF2 are hub genes.


HAGLROS is a potential biomarker for early diagnosis and prognosis evaluation of ovarian cancer. It can be used as a molecular sponge of miR-100 to regulate the expression of mTOR and ZNRF2 and affect the signal transduction of the mTOR pathway. HAGLROS is expected to be a new target for the treatment of ovarian cancer.


Long noncoding RNA HAGLROS Ovarian cancer miR-100 mTOR signalling pathway ZNRF2 


Author contributions

All the authors contributed to the study conception and design. MY performed the experiments and ZZ collected and analysed the data. YW and YZ contributed to the quality control of data and algorithms. The first draft of the manuscript was written by MY and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.


This study was funded by the Science and Technology Commission of Henan Province (Grant number: 162102310174).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the Ethics Committee of the People’s Hospital of Zhengzhou University.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Pisanic TR II, Cope LM, Lin SF, Yen TT, Athamanolap P, Asaka R, Nakayama K, Fader AN, Wang TH, Shih IM, Wang TL (2018) Methylomic analysis of ovarian cancers identifies tumor-specific alterations readily detectable in early precursor lesions. Clin Cancer Res 24(24):6536–6547. CrossRefGoogle Scholar
  2. 2.
    Blyuss O, Burnell M, Ryan A, Gentry-Maharaj A, Marino IP, Kalsi J, Manchanda R, Timms JF, Parmar M, Skates SJ, Jacobs I, Zaikin A, Menon U (2018) Comparison of longitudinal CA125 algorithms as a first-line screen for ovarian cancer in the general population. Clin Cancer Res 24(19):4726–4733. CrossRefGoogle Scholar
  3. 3.
    Günel T, Gumusoglu E, Dogan B, Ertem FB, Hosseini MK, Cevik N, Senol T, Topuz S, Aydinli K (2018) Potential biomarker of circulating hsa-miR-1273g-3p level for detection of recurrent epithelial ovarian cancer. Arch Gynecol Obstet 298(6):1173–1180CrossRefGoogle Scholar
  4. 4.
    Eisenhauer EA (2017) Real-world evidence in the treatment of ovarian cancer. Ann Oncol 28(8):61–65. CrossRefGoogle Scholar
  5. 5.
    Chan JJ, Tay Y (2018) Noncoding RNA:RNA regulatory networks in cancer. Int J Mol Sci 19(5):1310. CrossRefGoogle Scholar
  6. 6.
    Tay Y, Rinn J, Pandolfi PP (2014) The multilayered complexity of ceRNA crosstalk and competition. Nature 505(7483):344–352. CrossRefGoogle Scholar
  7. 7.
    Ulitsky I (2018) Interactions between short and long noncoding RNAs. FEBS Lett 592(17):2874–2883. CrossRefGoogle Scholar
  8. 8.
    Tripathi MK, Doxtater K, Keramatnia F, Zacheaus C, Yallapu MM, Jaggi M, Chauhan SC (2018) Role of lncRNAs in ovarian cancer: defining new biomarkers for therapeutic purposes. Drug Discov Today 23(9):1635–1643CrossRefGoogle Scholar
  9. 9.
    Chang L, Guo R, Yuan Z, Shi H, Zhang D (2018) LncRNA HOTAIR regulates CCND1 and CCND2 expression by sponging miR-206 in ovarian cancer. Cell Physiol Biochem 49(4):1289–1303. CrossRefGoogle Scholar
  10. 10.
    Li N, Zhan X, Zhan X (2018) The lncRNA SNHG3 regulates energy metabolism of ovarian cancer by an analysis of mitochondrial proteomes. Gynecol Oncol 150(2):343–354CrossRefGoogle Scholar
  11. 11.
    Yan H, Li H, Li P, Li X, Lin J, Zhu L, Silva MA, Wang X, Wang P, Zhang Z (2018) Long noncoding RNA MLK7-AS1 promotes ovarian cancer cells progression by modulating miR-375/YAP1 axis. J Exp Clin Cancer Res 37(1):237CrossRefGoogle Scholar
  12. 12.
    Chen JF, Wu P, Xia R, Yang J, Huo XY, Gu DY, Tang CJ, De W, Yang F (2018) STAT3-induced lncRNA HAGLROS overexpression contributes to the malignant progression of gastric cancer cells via mTOR signal-mediated inhibition of autophagy. Mol Cancer 17(1):6. CrossRefGoogle Scholar
  13. 13.
    Zheng Y, Tan K, Huang H (2019) Long noncoding RNA HAGLROS regulates apoptosis and autophagy in colorectal cancer cells via sponging miR-100 to target ATG5 expression. J Cell Biochem 120(3):3922–3933. CrossRefGoogle Scholar
  14. 14.
    Sanguesa G, Roglans N, Baena M, Velazquez AM, Laguna JC, Alegret M (2019) mTOR is a key protein involved in the metabolic effects of simple sugars. Int J Mol Sci 20(5):1117. CrossRefGoogle Scholar
  15. 15.
    Hoxhaj G, Caddye E, Najafov A, Houde VP, Johnson C, Dissanayake K, Toth R, Campbell DG, Prescott AR, MacKintosh C (2016) The E3 ubiquitin ligase ZNRF2 is a substrate of mTORC1 and regulates its activation by amino acids. Elife 5:e12278. CrossRefGoogle Scholar
  16. 16.
    Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4:e05005. CrossRefGoogle Scholar
  17. 17.
    Wong N, Wang X (2015) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 43(D1):D146–D152. CrossRefGoogle Scholar
  18. 18.
    Sticht C, De La Torre C, Parveen A, Gretz N (2018) miRWalk: an online resource for prediction of microRNA binding sites. PLoS ONE 13(10):e0206239. CrossRefGoogle Scholar
  19. 19.
    Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, Chiew MY, Tai CS, Wei TY, Tsai TR, Huang HT, Wang CY, Wu HY, Ho SY, Chen PR, Chuang CH, Hsieh PJ, Wu YS, Chen WL, Li MJ, Wu YC, Huang XY, Ng FL, Buddhakosai W, Huang PC, Lan KC, Huang CY, Weng SL, Cheng YN, Liang C, Hsu WL, Huang HD (2018) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 46(D1):D296–D302. CrossRefGoogle Scholar
  20. 20.
    Vejnar CE, Blum M, Zdobnov EM (2013) miRmap web: comprehensive microRNA target prediction online. Nucleic Acids Res 41:W165–168. CrossRefGoogle Scholar
  21. 21.
    Tripathi S, Pohl MO, Zhou Y, Rodriguez-Frandsen A, Wang G, Stein DA, Moulton HM, DeJesus P, Che J, Mulder LC, Yanguez E, Andenmatten D, Pache L, Manicassamy B, Albrecht RA, Gonzalez MG, Nguyen Q, Brass A, Elledge S, White M, Shapira S, Hacohen N, Karlas A, Meyer TF, Shales M, Gatorano A, Johnson JR, Jang G, Johnson T, Verschueren E, Sanders D, Krogan N, Shaw M, Konig R, Stertz S, Garcia-Sastre A, Chanda SK (2015) Meta- and orthogonal integration of influenza "OMICs" data defines a role for UBR4 in virus budding. Cell Host Microbe 18(6):723–735. CrossRefGoogle Scholar
  22. 22.
    Lin C, Yang L (2018) Long noncoding RNA in cancer: wiring signaling circuitry. Trends Cell Biol 28(4):287–301. CrossRefGoogle Scholar
  23. 23.
    Chistiakov DA, Myasoedova VA, Grechko AV, Melnichenko AA, Orekhov AN (2018) New biomarkers for diagnosis and prognosis of localized prostate cancer. Semin Cancer Biol 52(Pt 1):9–16. CrossRefGoogle Scholar
  24. 24.
    Panoutsopoulou K, Avgeris M, Scorilas A (2018) miRNA and long non-coding RNA: molecular function and clinical value in breast and ovarian cancers. Expert Rev Mol Diagn 18(11):963–979CrossRefGoogle Scholar
  25. 25.
    Rad E, Murray JT, Tee AR (2018) Oncogenic signalling through mechanistic target of rapamycin (mTOR): a driver of metabolic transformation and cancer progression. Cancers (Basel) 10(1):1. CrossRefGoogle Scholar
  26. 26.
    Li H, Zeng J, Shen K (2014) PI3K/AKT/mTOR signaling pathway as a therapeutic target for ovarian cancer. Arch Gynecol Obstet 290(6):1067–1078. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Gynecology and ObstetricsPeople’s Hospital of Zhengzhou University, Henan Provincial People’s HospitalZhengzhouChina
  2. 2.Department of Hepato-Biliary-Pancreatic SurgeryPeople’s Hospital of Zhengzhou University, Henan Provincial People’s HospitalZhengzhouChina

Personalised recommendations