Small-cell lung cancer (SCLC) is a type of lung cancer with early metastasis, and high recurrence and mortality rates. The molecular mechanism is still unclear and further research is required. The aim of the present study was to examine the pathogenesis and potential molecular markers of SCLC by comparing the differential expression of mRNA and microRNA (miRNA) between SCLC tissue and normal lung tissue. A transcriptome sequencing dataset (GSE6044) and a non-coding RNA sequence dataset (GSE19945) were downloaded from the Gene Expression Omnibus (GEO) database. In total, 451 differentially expressed genes (DEGs) and 134 differentially expressed miRNAs (DEMs) were identified using the R limma software package and the GEO2R tool of the GEO, respectively. The Gene Ontology function was significantly enriched for 28 terms, and the Kyoto Encyclopedia of Genes and Genomes database had 19 enrichment pathways, mainly related to ‘cell cycle’, ‘DNA replication’ and ‘oocyte meiosis mismatch repair’. The protein-protein interaction network was constructed using Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities in SCLC. The 1,402 miRNA-gene pairs encompassed 602 target genes of the DEMs using miRNAWalk, which is a bioinformatics platform that predicts DEM target genes and miRNA-gene pairs. There were 19 overlapping genes regulated by 32 miRNAs between target genes of the DEMs and DEGs. Bioinformatics analysis may help to better understand the role of DEGs, DEMs and miRNA-gene pairs in cell proliferation and signal transduction. The related hub genes may be used as biomarkers for the diagnosis and prognosis of SCLC, and as potential drug targets.

Background. T cell immunoglobulin and ITIM domain (TIGIT) is a recently identified immunosuppressive receptor. The expression levels of TIGIT affect the prognosis of patients with solid tumors. To fully comprehend the role of TIGIT on the prognosis of patients with solid tumors, we conducted a meta-analysis. Methods. We performed an online search of PubMed, Embase, Web of Science (WOS), and MEDLINE databases for literature published till March 31, 2021. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the literature, and Stata 16.0 and Engauge Digitizer 4.1 software were used for data analysis. Results. Our literature search identified eight papers comprising 1426 patients with solid tumors. Increased expression of TIGIT was associated with poor prognosis. High expression of TIGIT was a risk factor for overall survival (OS) { hazard   ratio   HR = 1.66 , 95% confidence interval (CI) [1.26, 2.20], P < 0.001 } and progression-free survival (PFS) ( HR = 1.44 , 95% CI [1.15, 1.81], P = 0.01 ). We performed subgroup analysis to explore the source of heterogeneity, colorectal cancer ( HR = 2.07 , 95% CI [0.23, 18.82], P = 0.518 ), lung cancer ( HR = 1.29 , 95% CI [0.96, 1.72], P = 0.094 ), esophageal cancer ( HR = 1.70 , 95% CI [1.20, 2.40], P = 0.003 ), and other cancers ( HR = 1.83 , 95% CI [1.25, 2.68], P = 0.002 ). In addition to cancer type, expression location, sample size, and different statistical analysis methods are also considered the possible causes of heterogeneity between studies. Funnel plots suggested no publication bias for OS ( P = 0.902 ), and Egger’s test supported this conclusion ( P = 0.537 ). Conclusion. TIGIT expression was associated with OS and PFS in patients with solid tumors. Patients with elevated TIGIT expression have a shorter OS and PFS, and TIGIT expression could be a novel biomarker for prognosis prediction and a valuable therapeutic target for solid tumors.

Cinobufotalin injection, extracted from the skin of Chinese giant salamander or black sable, has good clinical effect against lung cancer. However, owing to its complex composition, the pharmacological mechanism of cinobufotalin injection has not been fully clarified. This study aimed to explore the mechanism of action of cinobufotalin injection against lung cancer using network pharmacology and bioinformatics. Compounds of cinobufotalin injection were determined by literature retrieval, and potential therapeutic targets of cinobufotalin injection were screened from Swiss Target Prediction and STITCH databases. Lung-cancer-related genes were summarized from GeneCards, OMIM, and DrugBank databases. The pharmacological mechanism of cinobufotalin injection against lung cancer was determined by enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network was constructed. We identified 23 compounds and 506 potential therapeutic targets of cinobufotalin injection, as well as 70 genes as potential therapeutic targets of cinobufotalin injection in lung cancer by molecular docking. The antilung cancer effect of cinobufotalin injection was shown to involve cell cycle, cell proliferation, antiangiogenesis effect, and immune inflammation pathways, such as PI3K-Akt, VEGF, and the Toll-like receptor signaling pathway. In network analysis, the hub targets of cinobufotalin injection against lung cancer were identified as VEGFA, EGFR, CCND1, CASP3, and AKT1. A network diagram of “drug-compounds-target-pathway” was constructed through network pharmacology to elucidate the pharmacological mechanism of the antilung cancer effect of cinobufotalin injection, which is conducive to guiding clinical medication.