Construction of a New Immune-Related Competing Endogenous RNA Network with Prognostic Value in Lung Adenocarcinoma.

Tumor microenvironment has significant influence on the gene expression of tumor tissues and on the clinical outcomes in lung adenocarcinoma. Infiltrating immune and stromal cells not only perturb the tumor signal in molecular studies, but also play crucial roles in cancer biology. The competing endogenous RNAs (ceRNAs) are useful to explain the post-transcriptional layer regulated by gene translation and play an important role in the occurrence and progression of lung adenocarcinoma. Therefore, identifying novel molecular markers by constructing ceRNA associated with immune infiltration is of great significance to guide the treatment of lung adenocarcinoma in the future. According to the immune and stromal scores of lung adenocarcinoma samples in The Cancer Genome Atlas (TCGA) database calculated by the ESTIMATE algorithm, we identified differentially expressed lncRNAs, miRNAs and mRNAs associated with immune infiltration, including 60 dysregulated lncRNAs, 38 dysregulated mRNAs, and 29 dysregulated miRNAs. Based on the PPI network and Cox regression analysis, 5 mRNAs including CNR2, P2RY12, ZNF831, RSPO1, and F2 were identified to be related to immune infiltration and prognosis in lung adenocarcinoma, and their differential expression, prognosis and correlation with immune cells were verified. Next, through target binding prediction, pearson correlation analysis and expression analysis, a novel immune-related ceRNA network containing 6 lncRNAs, 4 miRNAs, and 3 mRNAs was finally constructed. The present study constructed a novel immune-associated lncRNA-miRNA-mRNA ceRNA network, which deepens our understanding on the molecular network mechanism of lung adenocarcinoma and provides potential prognostic markers and novel therapeutic targets for the patients with lung adenocarcinoma.

Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation.

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges caused by various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architectures have been proposed and widely used, but their performance remains unsatisfactory. To further address these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations. (1) Since the discrimination of the features learned via square convolutional kernels needs to be further improved, we propose utilizing nonsquare vertical and horizontal convolutional kernels in a double-branch encoder so that the features learned by both branches can be expected to complement each other. (2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation of small-sized targets. With the above two schemes, we develop a novel double-branch encoder-based segmentation framework for medical image segmentation, namely, Crosslink-Net, and validate its effectiveness on five datasets with experiments. The code is released at https://github.com/Qianyu1226/Crosslink-Net.

Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics.

OBJECTIVE: To differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity. METHODS: 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features. RESULTS: 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93. CONCLUSION: The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.

SMYD3 overexpression indicates poor prognosis and promotes cell proliferation, migration and invasion in non­small cell lung cancer.

SET and MYND domain­containing protein 3 (SMYD3) is a lysine methyltransferase, and its aberrant expression has been implicated in several malignancies. However, its clinical and biological roles in non­small cell lung cancer (NSCLC) remain unclear. In the present study, it was revealed that SMYD3 was significantly upregulated in NSCLC tissues, as compared with paired adjacent normal tissues. A high SMYD3 expression was associated with aggressive clinicopathological characteristics, as well as poor disease­free survival and overall survival (OS) in NSCLC patients. Multivariate analysis revealed that SMYD3 overexpression was an independent predictor of poor OS in NSCLC patients. In addition, SMYD3 knockdown inhibited cell proliferation, triggered apoptosis, and blocked migration and invasion in NSCLC cells in vitro, whereas stable SMYD3 overexpression promoted NSCLC cell proliferation. Furthermore, the SMYD3­silenced NSCLC cells became more sensitive, whereas the SMYD3­overexpressed NSCLC cells became more resistant to the apoptosis induced by cisplatin. Mechanistic analysis revealed that SMYD3 knockdown led to the upregulation of Bim, Bak and Bax, and the downregulation of Bcl­2, Bcl­xl, MMP­2 and MMP­9 in NSCLC cells. In combination, the present findings indicated that SMYD3 has oncogenic potential in the context of NSCLC, providing evidence that may be exploited for both prognostic and therapeutic purposes in the future.

MicroRNA­208a directly targets Src kinase signaling inhibitor 1 to facilitate cell proliferation and invasion in non­small cell lung cancer.

The abnormal expression of microRNAs (miRNAs/miRs) has a critical function in the formation and progression of non­small cell lung cancer (NSCLC). Therefore, understanding the association between NSCLC and dysregulated miRNAs may allow for the identification of novel diagnostic and therapeutic biomarkers for patients with this malignancy. Previous studies have validated miR­208a as a cancer­associated miRNA in multiple different types of human cancer, however, its expression pattern and precise function in NSCLC remains yet to be elucidated. Therefore, the aims of the present study were to measure miR­208a expression in NSCLC, investigate its specific functions in NSCLC and determine its exact regulatory mechanisms. Herein, the results demonstrated that miR­208a was significantly upregulated in NSCLC tissues and cell lines compared with that in adjacent non­cancerous tissues and a non­tumorigenic bronchial epithelium BEAS­2B cell line (P<0.05, respectively). The high expression level of miR­208a exhibited an obvious association with Tumor­Node­Metastasis stage and lymph node metastasis. MiR­208a silencing decreased the proliferative and invasive capacities of NSCLC cells. Notably, Src kinase signaling inhibitor 1 (SRCIN1) was verified as a potential direct target gene of miR­208a in NSCLC cells. Furthermore, SRCIN1 knockdown was able to rescue the miR­208a­mediated effects on NSCLC cells. In addition to this, silencing miR­208a expression inhibited the extracellular regulated kinase (ERK) signaling pathway in NSCLC. Overall, to the best of our knowledge, the present study is the first to provide evidence that miR­208a exerts oncogenic functions in the carcinogenesis and progression of NSCLC by directly targeting SRCIN1 and regulating the ERK pathway. Therefore, miR­208a may be developed as a potential target for treating patients with NSCLC.