The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every dataset. The RobustRankAggreg package was used to integrate the DEGs from the four datasets. Functional and pathway enrichment analyses were conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases with FunRich software and R software, respectively. In addition, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database and Cytoscape software. Hub genes and significant modules were identified using Molecular Complex Detection in Cytoscape software. Finally, the expression values of hub genes were determined using the Oncomine online database. In total, 412 DEGs were identified following the integration of the four datasets, with 146 upregulated genes and 266 downregulated genes. The upregulated DEGs were primarily enriched in the cell cycle, cell division and microtubule binding. The downregulated DEGs were primarily enriched in the complement and coagulation cascades, the cytokine-mediated signaling pathway and protein binding. Eight hub genes and 1 significant module correlated to the cell cycle pathway were identified based on a subset of the PPI network. Finally, five hub genes were identified as highly expressed in SCLC tissue compared with normal tissue. The cell cycle pathway may be the pathway most closely associated with the pathogenesis of SCLC. NDC80, BUB1B, PLK1, CDC20 and MAD2L1 should be the focus of follow-up studies regarding the diagnosis and treatment of SCLC.

Background: An increasing number of studies have shown that the positive lymph node ratio (pLNR) can be used to evaluate the prognosis of non-small cell lung cancer (NSCLC) patients. To determine the predictive value of the pLNR, we collected data from the Surveillance, Epidemiology, and End Results (SEER) database and performed a retrospective analysis. Methods: We collected survival and clinical information on patients with T 1−4 N 1−3 M 0 NSCLC diagnosed between 2010 and 2016 from the SEER database and screened them according to inclusion and exclusion criteria. X-tile software was used to obtain the best cutoff value for the pLNR. Then, we randomly divided patients into a training set and a validation set at a ratio of 7:3. Pearson's correlation coefficient, tolerance and the variance inflation factor (VIF) were used to detect collinearity between variables. Univariate and multivariate Cox regression analyses were used to identify significant prognostic factors, and nomograms was constructed to visualize the results. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomogram. We divided the patient scores into four groups according to the interquartile interval and constructed a survival curve using Kaplan-Meier analysis. Results: A total of 6,245 patients were initially enrolled. The best cutoff value for the pLNR was determined to be 0.55. The nomogram contained 13 prognostic factors, including the pLNR. The pLNR was identified as an independent prognostic factor for both overall survival (OS) and cancer-specific survival (CSS). The C-index was 0.703 (95% CI, 0.695-0.711) in the training set and 0.711 (95% CI, 0.699-0.723) in the validation set. The calibration curves and DCA also indicated the good predictability of the nomogram. Risk stratification revealed a statistically significant difference among the four groups of patients divided according to quartiles of risk score. Conclusion: The nomogram containing the pLNR can accurately predict survival in patients with T 1−4 N 1−3 M 0 NSCLC.

Acute lung injury (ALI) is a common clinical disease with high morbidity in both humans and animals. Studies have shown that intestinal microbiota affect the pathology and immune function of respiratory diseases through the "gut-lung axis". The authors investigated the therapeutic effect of fecal microbiota transplantation (FMT) in rats with ALI induced by lipopolysaccharide (LPS). Rats were treated with FMT, and then measured lung wet/dry ratio, PaO 2 in artery, proinflammatory marker, and TGF-β1, Smad3, Smad7, and phosphorylated ERK (p-ERK) protein levels, as well as a histopathologic analysis and high-throughput sequencing of intestinal microbiota. FMT significantly reduced lung wet/dry ratio and TNF-α, IL-1β, and IL-6 levels, but increased the levels of PaO 2 in artery. In addition, FMT significantly decreased the expression of TGF-β1, Smad3, and p-ERK, while increased the levels of Smad7. Lung histopathological analyses showed that FMT reduced the inflammatory cell infiltration and interstitial lung exudates. High-throughput sequencing of intestinal microbiota analyses showed that FMT reconstructed the structure of intestinal microbiota, and increased the gene abundance of the bacterial community. Therefore, FMT may act on the TGF-β1/Smads/ERK pathway by regulating intestinal microbiota, inhibiting immune inflammation, reducing the production of inflammatory markers in the body and release, and reducing alveolar epithelial damage and repair, thereby improving the endotoxic ALI in rats induced by LPS.

As a malignant tumor with poor prognosis, accurate and effective treatment of non-small cell lung cancer (NSCLC) is crucial. To predict overall survival in patients with stage II and III NSCLC, a nomogram was constructed using data from the Surveillance, Epidemiology and End Results database. Eligible patients with NSCLC with available clinical information diagnosed between January 1, 2010 and November 31, 2015 were selected from the database, and the data were randomly divided into a training set and a validation set. Univariate and multivariate Cox regression analyses were used to identify prognostic factors with a threshold of P<0.05, and a nomogram was constructed. Harrell's concordance indexes and calibration plots were used to verify the predictive power of the model. Risk group stratification by stage was also performed. A total of 15,344 patients with stage II and III NSCLC were included in the study. The 3- and 5-year survival rates were 0.382 and 0.278, respectively. The training and validation sets comprised 10,744 and 4,600 patients, respectively. Age, sex, race, marital status, histology, grade, Tumor-Node-Metastasis T and N stage, surgery type, extent of lymph node dissection, radiation therapy and chemotherapy were identified as prognostic factors for the construction of the nomogram. The nomogram exhibited a clinical predictive ability of 0.719 (95% CI, 0.718–0.719) in the training set and 0.721 (95% CI, 0.720–0.722) in the validation set. The predicted calibration curve was similar to the standard curve. In addition, the nomogram was able to divide the patients into groups according to stage IIA, IIB, IIIA, and IIIB NSCLC. Thus, the nomogram provided predictive results for stage II and III NSCLC patients and accurately determined the 3- and 5-year overall survival of patients.

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