UBE2T promotes autophagy via the p53/AMPK/mTOR signaling pathway in lung adenocarcinoma

Abstract

Background Ubiquitin-conjugating enzyme E2T (UBE2T) acts as an oncogene in various types of cancer. However, the mechanisms behind its oncogenic role remain unclear in lung cancer. This study aims to explore the function and clinical relevance of UBE2T in lung cancer. Methods Lentiviral vectors were used to mediate UBE2T depletion or overexpress UBE2T in lung cancer cells. CCK8 analysis and western blotting were performed to investigate the effects of UBE2T on proliferation, autophagy, and relevant signaling pathways. To exploit the clinical significance of UBE2T, we performed immunohistochemistry staining with an anti-UBE2T antibody on 131 NSCLC samples. Moreover, we downloaded the human lung adenocarcinoma (LUAD) dataset from The Cancer Atlas Project (TCGA). Lasso Cox regression model was adopted to establish a prognostic model with UBE2T-correlated autophagy genes. Results We found that UBE2T stimulated proliferation and autophagy, and silencing this gene abolished autophagy in lung cancer cells. As suggested by Gene set enrichment analysis, we observed that UBE2T downregulated p53 levels in A549 cells and vice versa. Blockade of p53 counteracted the inhibitory effects of UBE2T depletion on autophagy. Meanwhile, the AMPK/mTOR signaling pathway was activated during UBE2T-mediated autophagy, suggesting that UBE2T promotes autophagy via the p53/AMPK/mTOR pathway. Interestingly, UBE2T overexpression increased cisplatin-trigged autophagy and led to cisplatin resistance of A549 cells, whereas inhibiting autophagy reversed drug resistance. However, no association was observed between UEB2T and overall survival in a population of 131 resectable NSCLC patients. Therefore, we developed and validated a multiple gene signature by considering UBE2T and its relevance in autophagy in lung cancer. The risk score derived from the prognostic signature significantly stratified LUAD patients into low- and high-risk groups with different overall survival. The risk score might independently predict prognosis. Interestingly, nomogram and decision curve analysis demonstrated that the signature’s prognostic accuracy culminated while combined with clinical features. Finally, the risk score showed great potential in predicting clinical chemosensitivity. Conclusions We found that UBE2T upregulates autophagy in NSCLC cells by activating the p53/AMPK/mTOR signaling pathway. The clinical predicting ability of UBE2T in LUAD can be improved by considering the autophagy-regulatory role of UBE2T.

UBE2T regulates FANCI monoubiquitination to promote NSCLC progression by activating EMT

“…Recently, UBE2T was reported to promote β-catenin nuclear translocation in HCC through the MAPK/ERK axis ( 40 ). In addition, UBE2T promotes the progression of LUAD by regulating autophagy through the p53/AMPK/mTOR signaling pathway ( 41 ). As a member of the E2 family, UBE2T enhances DNA crosslinking-induced damage repair by monoubiquitinating the FANCI-FANCD2 complex ( 42 ).…”

Knockdown of hsa_circ_0000729 Inhibits the Tumorigenesis of Non-Small Cell Lung Cancer Through Mediation of miR-1281/FOXO3 Axis

“…8 LUAD is the main subtype of NSCLC. 9 Nevertheless, the function of hsa_circ_0000729 in NSCLC remains largely unknown.…”

LncRNA HAR1A Suppresses the Development of Non-Small Cell Lung Cancer by Inactivating the STAT3 Pathway

“…Using the same method, Shen and colleagues also developed reliable gene PGSs for LUSC and glioblastoma, respectively [ 31 ]. Moreover, our team previously reported that UBE2T promoted autophagy in NSCLC, and a signature of 18 UBE2T-related autophagy genes greatly enhanced the predictive accuracy of prognosis compared to the UBE2T gene alone [ 19 ]. Taken together, these results suggest that genes critical for cancer cell survival are refined candidate biomarkers for prognostic prediction.…”

“…The remaining survival genes were entered into a LASSO regression model, and genes with non-zero coefficients were used to establish a multi-gene survival model. The resulting LASSO coefficients and corresponding patient expression levels of each included gene were incorporated into a risk score algorithm to quantitatively evaluate patient risk as published previously [ 16 , 19 , 20 ]. To assess the predictive ability of the prognostic multiple gene models, time-dependent receiver operating characteristic (ROC) curves were plotted for the risk scores with the utilization of the R package survivalROC.…”