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Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools., (© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.)

The incidence of lung cancer is increasing worldwide. Although great progress in lung cancer treatment has been made, the clinical outcome is still unsatisfactory. Tripartite motif (TRIM)-containing proteins has been shown to be closely related to tumor progression. However, the function of TRIM46 in lung cancer is largely unknown. Here, TRIM46 amplification was found in lung adenocarcinoma (LUAD) tissues and TRIM46 amplification was significantly associated with a poor survival rate. Overexpression of wild type TRIM46 increased the proliferation of LUAD cells and glycolysis, promoted xenografts growth, and enhanced cisplatin (DDP) resistance of LUAD cells via increased ubiquitination of pleckstrin homology domain leucine-rich repeat protein phosphatase 2 (PHLPP2) and upregulation of p-AKT. In contrast, overexpression of RING-mutant TRIM46 did not show any effects, suggesting the function of TRIM46 was dependent on the E3 ligase activity. Furthermore, we found that TRIM46 promoted LUAD cell proliferation and DDP resistance by enhancing glycolysis. PHLPP2 overexpression reversed the effects of TRIM46 overexpression. Amplification of TRIM46 also promoted LUAD growth and enhanced its DDP resistance in a patient-derived xenograft (PDX) model. In conclusion, our data highlight the importance of TRIM46/PHLPP2/AKT signaling in lung cancer and provide new insights into therapeutic strategies for lung cancer., (© 2022. The Author(s).)

Background: Long noncoding RNAs (lncRNAs) have gain increasing attention in lung adenocarcinoma. In this study, we aimed at constructing and analyzing the lncRNAs and the related proteins based competitive endogenous RNA (ceRNA) network., Methods: RNA expression data of lung adenocarcinoma were extracted from the TCGA database. Differentially expressed (DE) lncRNAs, messenger RNAs (mRNAs) and microRNAs (miRNAs) were identified and then a DElncRNA-DEmiRNA-DEmRNA ceRNA network was constructed for lung adenocarcinoma. We also analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEgenes. Kaplan-Meier survival curves were also been further utilized for exploring the prognostic factors., Results: After compared and calculated lncRNA, mRNA and miRNA expression profiles between lung adenocarcinoma and normal samples, 1709 differential expressed lncRNAs, 2554 differential expressed mRNAs and 116 differential expressed miRNAs were finally identified. Afterwards, a lncRNA mediated ceRNA network was constructed, according to the interactions among 544 pairs of DElncRNA-DEmiRNA relationships and 47 pairs of DEmiRNA-DEmRNA relationships. As for the survival analyses, we found 10 DElncRNAs, 25 DEmRNAs and 7 miRNAs have statistically prognostic significance for overall survival, respectively., Conclusions: This study provides meaningful information for deeper understanding the underlying molecular mechanism of lung adenocarcinoma and for evaluating prognosis, which could monitor recurrence, guide clinical treatment drugs and subsequent related researches., Competing Interests: Declaration of Competing Interest None to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)

BACKGROUND We investigated the correlation between cavity formation, prognosis, and tumor stage for pathologic stage I invasive lung adenocarcinomas (IADCs) ≤3 cm in size. MATERIAL AND METHODS 2106 candidates with pathologic stage I IADC were identified from Shanghai Chest Hospital between 2009 and 2014. There were 227 patients who were diagnosed as having cavity formation and another 1879 patients who were not (the non-cavitary lung cancer group). Kaplan-Meier analysis curves were conducted to compare the overall survival (OS) and relapse-free survival (RFS) between these 2 groups. Cox proportional hazards regression was performed to discover the independent risk factors of OS and RFS. Receiver operating characteristic (ROC) curve was done to determine the cutoff value of cavity size for predicting prognosis. Furthermore, subgroup analysis was stratified by the size of tumor and the 8th classification of T category. RESULTS Compared with non-cavitary lung cancer group, patients with cavity formation were found to have a higher prevalence of male patients (P=0.015), older age patients (P=0.039), larger size tumors (P=0.004), and worse cancer relapse (P<0.001). Survival analysis found that patients with cavitary IADC had significantly shorter RFS than those with non-cavitary IADC (P=0.001). Further, subgroup analysis confirmed a significantly worse RFS in cavitary IADC group both in stage T1a (P=0.002) and T1b (P<0.001), but not for stage T1c (P=0.962) and T2a (P=0.364). Moreover, cavity formation was still less of a significant predictor of RFS in multivariable analysis (hazard ratio [HR] 1.810, 95% confidence level [CI] 1.229-2.665, P=0.003). The ROC curve showed that the best cutoff value of maximum diameter of the cavity for judging RFS was 5 mm (sensitivity: 0.500; specificity: 0.783). At the same time, multiple cavities were more likely to lead to recurrence (sensitivity: 0.605; specificity: 0.439). CONCLUSIONS Cavitary adenocarcinoma was a worse prognostic indicator compared with non-cavitary adenocarcinoma, especially for cavity >5 mm and multiple cavities. Thus, for stage T1a and T1b, cavitary and non-cavitary IADC should be considered separately.