Research Paper|Volume 16, Issue 11|pp 9918—9932

Mechanistic insights into super-enhancer-related genes as prognostic signatures in colon cancer

Yini Tang¹, Shuliu Sang², Shuang Gao³, Weina Xu⁴, Hailun Zhou², Xiaoting Xia⁵

¹Department of Endoscopy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
²Department of Oncology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
³Department of Anorectal Surgery, The Third Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Yunnan, China
⁴Department of TCM, Zhoujiadu Community Health Service of Shanghai Pudong New Area Center, Shanghai, China
⁵Department of Oncology, Shanghai TCM-intergrated Hospital, Shanghai, China

* Equal contribution and shared first authorship

Received: November 14, 2023Accepted: May 3, 2024Published: June 7, 2024

https://doi.org/10.18632/aging.205906

Copyright: © 2024 Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Colon cancer (CC) is the most frequently occurring digestive system malignancy and is associated with a dismal prognosis. While super-enhancer (SE) genes have been identified as prognostic markers in several cancers, their potential as practical prognostic markers for CC patients remains unexplored.

Methods: We obtained super-enhancer-related genes (SERGs) from the Human Super-Enhancer Database (SEdb). Transcriptome and relevant clinical data for colon cancer (CC) were sourced from the Gene Expression Omnibus (GEO) database. Subsequently, we identified up-regulated SERGs by the Weighted Gene Co-expression Network Analysis (WGCNA). Prognostic signatures were constructed via univariate and multivariate Cox regression analysis. We then delved into the mechanisms of these predictive genes by examining immune infiltration. We also assessed differential sensitivities to chemotherapeutic drugs between high- and low-SERGs risk patients. The critical gene was further validated using external datasets and finally confirmed by qRT PCR.

Results: We established a ten-gene risk score prognostic model (S100A11, LZTS2, CYP2S1, ZNF552, PSMG1, GJC1, NXN, and DCBLD2), which can effectively predict patient survival rates. This model demonstrated effective prediction capabilities in survival rates at 1, 3, and 5 years and was successfully validated using external datasets. Furthermore, we detected significant differences in immune cell infiltration between high- and low-SERGs risk groups. Notably, high-risk patients exhibited heightened sensitivity to four chemotherapeutic agents, suggesting potential benefits for precision therapy in CC patients. Finally, qRT-PCR validation revealed a significant upregulation of LZTS2 mRNA expression in CC cells.

Conclusion: These findings reveal that the SERGs model could effectively predict the prognosis of CC.