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. 2024 Dec;62(6):4654-4678.
doi: 10.1007/s10528-024-10728-2. Epub 2024 Feb 13.

Comprehensive Analysis of the SUMO-related Signature: Implication for Diagnosis, Prognosis, and Immune Therapeutic Approaches in Cervical Cancer

Xing Zhang #  1 , Jian Cao #  2 , Xiuting Li  3 , Yan Zhang  4 , Wenjing Yan  1 , Bo Ding  5 , Jing Hu  1 , Haohan Liu  1 , Xue Chen  1 , Yamei Nie  1 , Fengying Liu  1 , Ning Lin  6 , Shizhi Wang  7
Affiliations

Comprehensive Analysis of the SUMO-related Signature: Implication for Diagnosis, Prognosis, and Immune Therapeutic Approaches in Cervical Cancer

Xing Zhang et al. Biochem Genet. 2024 Dec.

Abstract

SUMOylation, an important post-translational protein modification, plays a critical role in cancer development and immune processes. This study aimed to construct diagnostic and prognostic models for cervical cancer (CC) using SUMOylation-related genes (SRGs) and explore their implications for novel clinical therapies. We analyzed the expression profiles of SRGs in CC patients and identified 15 SRGs associated with CC occurrence. After the subsequent qPCR verification of 20 cases of cancer and adjacent tissues, 13 of the 15 SRGs were differentially expressed in cancer tissues. Additionally, we identified molecular markers associated with the prognosis and recurrence of CC patients, based on SRGs. Next, a SUMOScore, based on SRG expression patterns, was generated to stratify patients into different subgroups. The SUMOScore showed significant associations with the tumor microenvironment, immune function features, immune checkpoint expression, and immune evasion score in CC patients, highlighting the strong connection between SUMOylation factors and immune processes. In terms of immune therapy, our analysis identified specific chemotherapy drugs with higher sensitivity in the subgroups characterized by high and low SUMOScore, indicating potential treatment options. Furthermore, we conducted drug sensitivity analysis to evaluate the response of different patient subgroups to conventional chemotherapy drugs. Our findings revealed enrichment of immune-related pathways in the low-risk subgroup identified by the prognostic model. In conclusion, this study presents diagnostic and prognostic models based on SRGs, accompanied by a comprehensive index derived from SRGs expression patterns. These findings offer valuable insights for CC diagnosis, prognosis, treatment, and immune-related analysis.

Keywords: Cervical Cancer; Immune; Machine Learning; Prognosis; SUMOylation.

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Conflict of interest statement

Declarations. Competing Interests: All authors report no conflict of interest. Ethics Approval: The study was conducted according to guidelines approved by the Institutional Review Board of Southeast University. All participants signed informed consent. The approval number is 2018ZDKYSB022. Consent to Participate: Informed consent was obtained from each participant within the scope of this study. Consent to Publish: The authors affirm that human research participants provided informed consent for publication of the images in Fig. 2 and Table S2.

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