Proteome-Wide Analysis Reveals TFEB Targets for Establishment of a Prognostic Signature to Predict Clinical Outcomes of Colorectal Cancer

TFEB 结直肠癌 生物 小桶 计算生物学 基因 生物信息学 癌症研究 转录因子 癌症 遗传学 转录组 基因表达
作者
Zi-Jia Huang,Sheng Zhu,Ziqin Han,Chen Li,Junze Liang,Yang Wang,Shuixing Zhang,Jing Zhang
出处
期刊:Cancers [Multidisciplinary Digital Publishing Institute]
卷期号:15 (3): 744-744 被引量:7
标识
DOI:10.3390/cancers15030744
摘要

Dephosphorylation of transcription factor EB (TFEB) at Ser142 and Ser138 determines its nuclear localization and transcriptional activity. The link between TFEB-associated genes and colorectal cancer (CRC) progression and prognosis remains unclear. To systematically identify the targets of TFEB, we performed data-independent acquisition (DIA)-based quantitative proteomics to compare global protein changes in wild-type (WT) DLD1 cells and TFEBWT- or TFEBS142A/S138A (activated status)-expressing DLD1 cells. A total of 6048 proteins were identified and quantified in three independent experiments. The differentially expressed proteins in TFEBS142A/S138A versus TFEBWT and TFEBWT versus control groups were compared, and 60 proteins were identified as products of TFEB transcriptional regulation. These proteins were significantly associated with vesicular endocytic trafficking, the HIF-1 signaling pathway, and metabolic processes. Furthermore, we generated a TFEB-associated gene signature using a univariate and LASSO Cox regression model to screen robust prognostic markers. An eight-gene signature (PLSCR3, SERPINA1, ATP6V1C2, TIMP1, SORT1, MAP2, KDM4B, and DDAH2) was identified. According to the signature, patients were assigned to high-risk and low-risk groups. Higher risk scores meant worse overall survival and higher epithelial–mesenchymal transition (EMT) scores. Additionally, as per the clinicopathological parameters and gene signature, a nomogram was constructed that was utilized to enhance the quantification capacity in risk assessment for individual patients. This research shows that TFEB directly mediates network effects in CRC, and the identified TFEB gene signature-based model may provide important information for the clinical judgment of prognosis.
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