A Novel Gene Signature Associated with Protein Post-translational Modification to Predict Clinical Outcomes and Therapeutic Responses of Colorectal Cancer

作者全名："Liu, Jun; Zhu, Peng"

作者地址："[Liu, Jun; Zhu, Peng] Chongqing Med Univ, Affiliated Hosp 2, Dept Gastrointestinal Surg, 74 Linjiang Rd, Chongqing 400010, Peoples R China"

通信作者："Zhu, P (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Gastrointestinal Surg, 74 Linjiang Rd, Chongqing 400010, Peoples R China."

来源：MOLECULAR BIOTECHNOLOGY

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:001050041200001

JCR分区：Q3

影响因子：2.4

年份：2023

卷号：　

期号：　

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结束页：　

文献类型：Article; Early Access

关键词：Colorectal cancer; Post-translational modification; Prognosis; Tumor microenvironment; Chemotherapy; Immunotherapy

摘要："Accumulated evidence highlights the biological significance of diverse protein post-translational modifications (PTMs) in tumorigenicity and progression of colorectal cancer (CRC). In this study, ten PTM patterns (ubiquitination, methylation, phosphorylation, glycosylation, acetylation, SUMOylation, citrullination, neddylation, palmitoylation, and ADP-ribosylation) were analyzed for model construction. A post-translational modification index (PTMI) with a 14-gene signature was established. CRC patients with high PTMI had a worse prognosis after validating in nine independent datasets. By incorporating PTMI with clinical features, a nomogram with excellent predictive performance was constructed. Two molecular subtypes of CRC with obvious difference in survival time were identified by unsupervised clustering. Furthermore, PTMI was related to known immunoregulators and key tumor microenvironment components. Low-PTMI patients responded better to fluorouracil-based chemotherapy and immune checkpoint blockade therapy compared to high-PTMI patients, which was validated in multiple independent datasets. However, patients with high PTMI might be sensitive to bevacizumab. In short, we established a novel PTMI model by comprehensively analyzing diverse post-translational modification patterns, which can accurately predict clinical prognosis and treatment response of CRC patients."

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