Building endoplasmic reticulum stress-related LncRNAs signatures of lung adenocarcinoma.

BACKGROUND: Endoplasmic reticulum stress (ERS) could be a strategy for treating malignant tumors. Moreover, long noncoding RNAs (lncRNAs) can promote tumorigenesis and progression, and forecast the prognosis of cancers. Nevertheless, the prognostic value of ERS-related lncRNAs has not been reported in lung adenocarcinoma (LUAD). METHODS: The messenger RNA (mRNA), microRNA (miRNA) and lncRNA expression data related to LUAD were obtained in public databases (TCGA and GEO databases). Prognostic ERS-related differentially expressed lncRNAs (ERS-DELs) were obtained and used to build an ERS-related model by Cox regression analysis. Moreover, we further screened independent prognostic elements and built a nomogram. Furthermore, enrichment analysis of genes was conducted to investigate the functions. A lncRNA-miRNA-mRNA network was built to explore mechanism of lncRNAs. Finally, qRT-PCR was utilized to examine the expression levels of lncRNAs. RESULTS: 30 ERS-DELs were identified, and an ERS-related signature was built based on AF131215.2, LINC00472, LINC01352, RP1-78O14.1, RP11-253E3.3, RP11-98D18.9, and SNHG12. Gene set enrichment analysis indicated that genes in the high-risk group were chiefly focused on the regulation of mRNA binding, and genes in the low-risk group were significantly focused on protein localization to cilia. A lncRNA-miRNA-mRNA network, containing 7 signature lncRNAs, 23 miRNAs, and 128 mRNAs, was also established. Eventually, quantitative real-time polymerase chain reaction was used to confirm that seven prognostic lncRNAs had a consistent expression with the analysis. CONCLUSIONS: An ERS-related signature containing seven prognostic lncRNAs was built, which offered new thinking concerning the role of ERS-related lncRNAs in LUAD.

Pulmonary Nodule Clinical Trial Data Collection and Intelligent Differential Diagnosis for Medical Internet of Things.

In this paper, the medical Internet of things (IoT) is used to pool data from clinical trials of pulmonary nodules, and on this basis, intelligent differential diagnosis techniques are investigated. A filtered orthogonal frequency division multiplexing model based on polarisation coding is proposed, where the input data are fed to a modulator after polarisation cascade coding, and the system performance is analysed under a medical Internet of things modulated additive Gaussian white noise channel. The above polarisation-coded filtered orthogonal frequency division multiplexing system components are applied to electroencephalogram (EEG) signal transmission, to which a threshold compression module and a vector reconstruction module are added to address the system power burden associated with the acquisition and transmission of large amounts of real-time EEG data in the medical IoT. In the threshold compression module, the inherent characteristics of EEG signals are analysed, and the generated EEG data are decomposed into multiple symbolic streams and compressed by applying different thresholds to improve the compression ratio while ensuring the quality of service of the application. A deep neural network-based approach is proposed for the detection and diagnosis of lung nodules. Automatic identification and measurement of simulated lung nodules and the corresponding volumes of nodules in images under different conditions are applied. The sensitivity of each AIADS in identifying lung nodules under different convolution kernel conditions, false positives (FP), false negatives (FN), relative volume errors (RVE), the miss detection rate (MDR) for different types of lung nodules, and the performance of each system in predicting the four types of nodules are calculated. In this paper, an interpretable multibranch feature convolutional neural network model is proposed for the diagnosis of benign and malignant lung nodules. It is demonstrated that the proposed model not only yields interpretable lung nodule classification results but also achieves better lung nodule classification performance with an accuracy rate of 97.8%.

The prognosis analysis of organ metastatic patterns in lung large cell neuroendocrine carcinoma: A population-based study.

Lung large cell neuroendocrine carcinoma (LCNEC) is a rare and highly aggressive malignancy with a dismal prognosis. This study was designed to depict patterns of distant organ metastatic and to analyze prognosis of LCNEC patients. We gathered data from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. We conducted the Kaplan-Meier method to calculate overall survival (OS) and compare different variables. Cox proportional hazards regression models in univariate and multivariate analyses were employed to further explore prognostic factors. A total of 1335 LCNEC patients were eventually selected from the SEER database, of which 348 patients (26.0%) had single organ metastasis and 197 patients (14.8%) had multiple metastases. Our study indicates that patients with single organ metastasis generally have a poor prognosis, with a median OS of 8 months for both lung and brain metastasis with 1-year survival rates of 33% and 29% respectively. Patients with multiple metastases exhibited the worst prognosis, with a median OS of only 4 months and a 1-year OS of 8%. Multivariate analysis revealed that age, T stage, N stage, chemotherapy and radiation in metastatic patients were independently associated with OS. In conclusion, LCNEC exhibits a high metastatic rate when diagnosed. The most common metastatic organ is the brain in single-site metastatic patients. Patients with single or multiple metastases exhibit a significantly worse prognosis than those with non-organ metastases. In the group of single organ metastases, patients with brain and lung metastases had a better prognosis than those with bone and liver metastases.

The Prognosis of Pulmonary Sarcomatoid Carcinoma: Development and Validation of a Nomogram Based on SEER.

Background: The rarity of pulmonary sarcomatoid carcinoma (PSC) and the lack of prospective clinical trials have led to limited knowledge of its clinical characteristics. This study aimed to evaluate the survival and prognostic factors of PSC and to build a nomogram for clinical practice. Methods: Eligible patients diagnosed from 2010 to 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. We compared the clinical characteristics and survival times of PSC patients with those of lung adenocarcinoma (LADC) and lung squamous cell carcinoma (LSCC) patients. We also used univariate and multivariable Cox regression to estimate mortality hazard ratios among patients with PSC, while a visual nomogram was established to judge the prognosis. Discrimination, calibration, clinical utility, and reproducibility were validated by Harrell's concordance index (C-index), the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: A total of 400 PSC patients (0.42%) were identified in the SEER database, whereas 58 474 and 33 637 patients were diagnosed with LADC and LSCC, respectively. Age, T stage, grade, surgery, and radiation were shown to be significant prognostic factors in the Cox regression analyses and were included in the nomogram as predictors. The C-index of the nomogram in the validation set was 0.759. The AUC also demonstrated the good performance of the nomogram, and DCA demonstrated its good clinical applicability. Conclusion: We established a novel nomogram to predict the prognosis of PSC, which can help clinicians make tailored decisions and adjust follow-up management strategies, and can provide accurate and individualized survival predictions.

miR-498/DNMT3b Axis Mediates Resistance to Radiotherapy in Esophageal Cancer Cells.

Objective: To explore the role of miR-498 in the radiotherapy resistance of esophageal cancer (EC) and its underlying mechanism. Methods: In vivo models of EC tissues with radioresistance or radiosensitivity were isolated from 72 EC patients who received radiotherapy. In vitro models were established after irradiation of KYSE30 cells. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blot were employed to measure the expression levels of miR-498 and DNMT3b in EC cells sensitive or resistant to irradiation. Then, protein expression of DNMT3b was verified by immunohistochemistry. The cell viability, colony formation rate, and cell apoptotic rate of EC were correspondingly assessed by CCK-8, colony formation assay, and Annexin V/PI (propidium iodide) double staining. Western blot was utilized to perform the expression levels of PI3K, p-PI3K, AKT, and p-AKT in EC cell lines after irradiation. Results: Highly expressed DNMT3b and lowly expressed miR-498 were found in EC tissues. EC tissues with radiosensitivity had higher miR-498 level and lower DNMT3b expression than EC tissues with radioresistance. Overexpression of miR-498 or knockdown of DNMT3b enhanced the radiosensitivity of EC cells. DNMT3b was a target gene of miR-498. DNMT3b diminished the radiosensitization of miR-498 in EC cells. Conclusions: MiR-498 enhances the sensitivity of EC cells to radiation by DNMT3b inhibition, and exerts biological functions by inactivating the PI3K/AKT signaling pathway.

Microarray analysis of long non-coding RNA expression profiles in Marfan syndrome.

Long non-coding RNAs (lncRNAs) serve a crucial role in every aspect of cell biological functions as well as in a variety of diseases, including cardiovascular disease, cancer and nervous system disease. However, the differential expression profiles of lncRNAs in Marfan syndrome (MFS) have not been reported. The aim of the present study was to identify potential target genes behind the pathogenesis of MFS by analyzing microarray profiles of lncRNA in aortic tissues from individuals with MFS and normal aortas (NA). The differentially expressed lncRNA profiles between MFS (n=3) and NA (n=4) tissues were analyzed using microarrays. Bioinformatics analyses were used to further investigate the candidate lncRNAs. Reverse transcription-quantitative (RT-qPCR) was applied to validate the results. In total, the present study identified 294 lncRNAs (245 upregulated and 49 downregulated) and 644 mRNAs (455 upregulated and 189 downregulated) which were differential expressed between MFS and NA tissues (fold change ≥1.5; P<0.05). Gene Ontology enrichment analysis indicated that the differentially expressed mRNAs were involved in cell adhesion, elastic fiber assembly, extracellular matrix (ECM) organization, the response to virus and the inflammatory response. Kyoto Encyclopedia of Gene and Genomes pathway analysis indicated that the differentially expressed mRNAs were mainly associated with focal adhesion, the ECM-receptor interaction, the mitogen-activated protein kinase signaling pathway and the tumor necrosis factor signaling pathway. The lncRNA-mRNA coexpression network analysis further elucidated the interaction between the lncRNAs and mRNAs. A total of five lncRNAs (uc003jka.1, uc003jox.1, X-inactive specific transcript, linc-lysophosphatidic acid receptor 1 and linc-peptidylprolyl isomerase domain and WD repeat containing 1) with the highest degree of coexpression were selected and confirmed using RT-qPCR. In the present study, expression profiles of lncRNA and mRNA in MFS were revealed using microarray analysis. These results provided novel candidates for further investigation of the molecular mechanisms and effective targeted therapies for MFS.

[The studies and progress of MEMS-based micro surgical tools].

The MEMS-based micro surgical tools are a kind of new devices applied in the minimally-invasive surgery. This paper introduces their application background and basic technology first, and then lays emphasis on the typical research accomplishments in the world. Finally, the research activities in China and some of the difficulties about the work are discussed.