Autophagy, a highly conserved cellular proteolysis process, has been involved in non-small cell lung cancer (NSCLC). We tried to develop a prognostic prediction model for NSCLC patients based on the expression profiles of autophagy-associated genes. Univariate Cox regression analysis was used to determine autophagy-associated genes significantly correlated with overall survival (OS) of the TCGA lung cancer cohort. LASSO regression was performed to build multiple-gene prognostic signatures. We found that the 22-gene and 11-gene signatures could dichotomize patients with significantly different OS and independently predict the OS in TCGA lung adenocarcinoma (HR=2.801, 95% CI=2.252-3.486, P<0.001) and squamous cell carcinoma (HR=1.105, 95% CI=1.067-1.145, P<0.001), respectively. The prognostic performance of the 22-gene signature was validated in four GEO lung cancer cohorts. Moreover, GO, KEGG, and GSEA analyses unveiled several fundamental signaling pathways and cellular processes associated with the 22-gene signature in lung adenocarcinoma. We also constructed a clinical nomogram with a concordance index of 0.71 to predict the survival possibility of NSCLC patients by integrating clinical characteristics and the autophagy gene signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. Overall, we constructed and verified a novel autophagy-associated gene signature that could improve the individualized outcome prediction in NSCLC.

N6-methyladenosine (m 6 A) RNA modification can alter gene expression and function by regulating RNA splicing, stability, translocation, and translation. Deregulation of m 6 A has been involved in various types of cancer. However, its implications in non-small-cell lung cancer (NSCLC) are mostly unknown. This posttranscriptional modification is dynamically and reversibly mediated by different regulators, including methyltransferase, demethylases, and m 6 A binding proteins. In this study, we comprehensively investigated the contributions and prognostic values of 13 common m 6 A RNA modification regulators using The Cancer Genome Atlas database. We found that the expression levels of most of the studied genes were significantly altered in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Using consensus clustering, the gene-expression profiles of 13 m 6 A regulators could classify patients with LUAD into two subgroups with significantly distinct clinical outcomes, but not the LUSC cohort or the combination of the two cohorts. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were applied to explore differential signaling pathways and cellular processes between the two LUAD subgroups. Moreover, we found that this gene-expression signature could better predict prognosis in the late-stage (III + IV) than in the early-stage (I + II) LUAD. Finally, we developed an optimal prognostic gene signature by using the least absolute shrinkage and selection operator Cox regression algorithm and compute risk score. In conclusion, our study unveiled the implication of m 6 A RNA modification regulators in NSCLC and identified the m 6 A gene expression classifiers for predicting the prognosis of NSCLC.

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.

Activated Cdc42-associated kinase1 (ACK1), a non-receptor tyrosine kinase, has been considered as an oncogene and therapeutic target in various cancers. However, its contribution to cancer immunity remains uncertain. Here we first compared the profiles of immune cells in cancerous and normal tissues in The Cancer Genome Atlas (TCGA) lung cancer cohorts. Next, we found that the immune cell infiltration levels were associated with the ACK1 gene copy numbers in lung cancer. Consistently, our RNA-seq data unveiled that the silencing of ACK1 upregulated several immune pathways in lung cancer cells, including the T cell receptor signaling pathway. The impacts of ACK1 on immune activity were validated by Gene Set Enrichment Analysis of RNA-seq data of 188 lung cancer cell lines from the public database. A pathway enrichment analysis of 35 ACK1-associated immunomodulators and 50 tightly correlated genes indicated the involvement of the PI3K-Akt and Ras signaling pathways. Based on ACK1-associated immunomodulators, we established multiple-gene risk prediction signatures using the Cox regression model. The resulting risk scores were an independent prognosis predictor in the TCGA lung cohorts. We also accessed the prognostic accuracy of the risk scores with a receiver operating characteristic methodology. Finally, a prognostic nomogram, accompanied by a calibration curve, was constructed to predict individuals' 3-and 5-year survival probabilities. Our findings provided evidence of ACK1's implication in tumor immunity, suggesting that ACK1 may be a potential immunotherapeutic target for non-small cell lung cancer (NSCLC). The nominated immune signature is a promising prognostic biomarker in NSCLC.

The aim of our study is to construct the competing endogenous RNA (ceRNA) network of head and neck squamous cell carcinoma (HNSCC) and identify key long noncoding RNAs (lncRNAs) to predict prognosis. The genes whose expression were differentially in HNSCC and normal tissues were explored by the Cancer Genome Atlas database. The ceRNA network was constructed by the Cytoscape software. The lncRNAs which could estimate the overall survival were explored from Cox proportional hazards regression. There are 1997, 589, and 82 mRNAs, lncRNAs, and miRNAs whose expression were statistically significant different, respectively. Then, the network between miRNA and mRNA or miRNA and lncRNA was constructed by miRcode, miRDB, TargetScan, and miRanda. Five mRNAs, 10 lncRNAs, and 3 miRNAs were associated with overall survival. Then, 11‐lncRNAs were found to be prognostic factors. Therefore, our research analyzed the potential signature of novel 11‐lncRNA as candidate prognostic biomarker from the ceRNA network for patients with HNSCC.