Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein-protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 downregulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.

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Overall survival of patients with low‐grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty‐eight nodes with 603 interactions were obtained from the establishment of protein‐protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients’ prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS‐275 (enrichment score = −0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG.

Objective To investigate the effect of a bilateral paravertebral block (PVB) on cognitive function in elderly patients undergoing radical gastrectomy for gastric cancer. Methods Sixty patients (40 men and 20 women) aged 65–80 undergoing radical gastrectomy surgery under general anaesthesia were included and randomly assigned to either the PVB group or the control group. Patients in the PVB group had before incision a single-shot ultrasound-guided bilateral PVB at the T8 level with 20 mL of ropivacaine 0.375%, while patients in the control group had no block. Patients in both groups had a BIS-guided total intravenous anaesthesia with propofol and remifentanil infusions. Postoperative cognitive function assessed by the mini-mental state examination (MMSE) and NSE (neuron-specific enolase) was the primary outcome. Results The awareness time in group PVB was shorter than that in the group C, and the propofol and remifentanil dosages were less than that in group C (P<0.001, P = 0.007, respectively). Furthermore, the change of the MMSE score and the NSE concentration was significant from day0 to day1 and day1 to day2. (P<0.001). Conclusion A single-shot bilateral PVB active throughout radical gastrectomy for gastric cancer reduces the needs for general anaesthetic agents and improve postoperative recovery, along with a surrogate evidence for neuroprotective effects. Trial registration ChiCTR2200060088. Registered 18 May 2022.

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.