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Establishing and Validating an Innovative Focal Adhesion-Linked Gene Signature for Enhanced Prognostic Assessment in Endometrial Cancer

  • Gynecologic Oncology: Original Article
  • Published:
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Abstract

Studies have highlighted the significant role of focal adhesion signaling in cancer. Nevertheless, its specific involvement in the pathogenesis of endometrial cancer and its clinical significance remains uncertain. We analyzed TCGA-UCEC and GSE119041 datasets with corresponding clinical data to investigate focal adhesion-related gene expression and their clinical significance. A signature, "FA-riskScore," was developed using LASSO regression in the TCGA cohort and validated in the GSE dataset. The FA-riskScore was compared with four existing models in terms of their prediction performance. We employed univariate and multivariate Cox regression analyses towards FA-riskScore to assess its independent prognostic value. A prognostic evaluation nomogram based on our model and clinical indexes was established subsequently. Biological and immune differences between high- and low-risk groups were explored through functional enrichment, PPI network analysis, mutation mining, TME evaluation, and single-cell analysis. Sensitivity tests on commonly targeted drugs were performed on both groups, and Connectivity MAP identified potentially effective molecules for high-risk patients. qRT-PCR validated the expressions of FA-riskScore genes. FA-riskScore, based on FN1, RELN, PARVG, and PTEN, indicated a poorer prognosis for high-risk patients. Compared with published models, FA-riskScore achieved better and more stable performance. High-risk groups exhibited a more challenging TME and suppressive immune status. qRT-PCR showed differential expression in FN1, RELN, and PTEN. Connectivity MAP analysis suggested that BU-239, potassium-canrenoate, and tubocurarine are effective for high-risk patients. This study introduces a novel prognostic model for endometrial cancer and offers insights into focal adhesion's role in cancer pathogenesis.

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Data Availability

Publicly available datasets were analyzed in this study. This data can be found here: (1) https://portal.gdc.cancer.gov/. (2) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119041.

Code Availability

All codes used in the study are available from the respective authors upon reasonable request.

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Author notes

  1. Cuiyin Yan and Leilei He contributed equally to this work.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China

    Cuiyin Yan & Leilei He

  2. Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China

    Yuhui Ma & Jing Cheng

  3. Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China

    Li Shen & Rajeev K Singla

  4. Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland

    Li Shen

  5. School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab, 144411, India

    Rajeev K Singla

  6. Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China

    Yueming Zhang

Authors
  1. Cuiyin Yan
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Contributions

YZ, LS, and RS conceptualized and designed this project. CY and LH established the model and performed further data analysis based on the TCGA dataset. LH and YM validated the established model based on the GEO dataset. JC and YM conducted wet-lab experimental validation. CY, LS, and RS wrote the whole manuscript. YZ contributed to the funding. All the authors have reviewed the manuscript and approved the submission.

Corresponding authors

Correspondence to Li Shen, Rajeev K Singla or Yueming Zhang.

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The authors declare that they have no conflicts of interest.

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The Ethics Committee of Dushu Lake Hospital, Affiliated with Soochow University, reviewed and approved this study involving human participants.

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Yan, C., He, L., Ma, Y. et al. Establishing and Validating an Innovative Focal Adhesion-Linked Gene Signature for Enhanced Prognostic Assessment in Endometrial Cancer. Reprod. Sci. (2024). https://doi.org/10.1007/s43032-024-01564-1

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