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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1452946
This article is part of the Research Topic Community Series in Novel Biomarkers in Tumor Immunity and Immunotherapy: Volume II View all 8 articles

Identification of telomere-related lncRNAs and immunological analysis in Ovarian cancer

Provisionally accepted
Weina Xu Weina Xu 1*Shuliu Sang Shuliu Sang 2Jun Wang Jun Wang 1*Shanshan Guo Shanshan Guo 3*Xiao Zhang Xiao Zhang 3*Hailun Zhou Hailun Zhou 2*Yijia Chen Yijia Chen 3*
  • 1 Zhoujiadu Community Health Service of Shanghai Pudong New Area, Shanghai, China
  • 2 Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
  • 3 Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai Municipality, China

The final, formatted version of the article will be published soon.

    Background: Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined. Methods: Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate / LASSO / multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram.Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell invasion assay, and wound healing assay of CAOV3 cells.A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets.According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells.Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.

    Keywords: ovarian cancer, prognosis, Telomere, immune, lncRNA

    Received: 21 Jun 2024; Accepted: 26 Aug 2024.

    Copyright: © 2024 Xu, Sang, Wang, Guo, Zhang, Zhou and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Weina Xu, Zhoujiadu Community Health Service of Shanghai Pudong New Area, Shanghai, China
    Jun Wang, Zhoujiadu Community Health Service of Shanghai Pudong New Area, Shanghai, China
    Shanshan Guo, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai Municipality, China
    Xiao Zhang, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai Municipality, China
    Hailun Zhou, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
    Yijia Chen, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai Municipality, China

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