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.

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.

Cinobufotalin injection is a water-soluble preparation extracted from the skin secretion of Bufo bufo gargarizans Cantor or B. melanotictus Schneider, which has been widely used as an adjuvant treatment in lung cancer patients. This study aimed to evaluate the clinical efficacy and safety of cinobufotalin (PubChem CID: 259776) injection as an adjunctive treatment for lung cancer. We designed a meta-analysis that performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We aim to include randomized controlled trials by systematically searching the PubMed, EMBASE, CNKI, Wanfang database, VIP, CBM, the Cochrane Central Register of Controlled Trials, and Chinese Clinical Trial Registry from inception to Mar 1, 2020, comparing the difference between the use of cinobufotalin injection as an adjunctive treatment and a control group without cinobufotalin injection. The objective response rate (ORR) and quality of life (QOL) will be defined as the primary outcomes, and the disease control rate (DCR) and adverse events will be defined as the secondary outcomes. We included 21 articles with 1735 cases of lung cancer patients. Comparison results show that combining with cinobufotalin injection can improve ORR (OR = 1.77, 95% CI [1.43, 2.21], P  < 0.001), with low heterogeneity ( P  = 0.94, I2 = 0%); DCR (OR = 2.20, 95% CI [1.70, 2.85], P  < 0.001), with low heterogeneity ( P  = 0.60, I2 = 0%); KPS score (OR = 3.10, 95% CI [2.23, 4.32], P  < 0.001), with low heterogeneity ( P  = 0.85, I2 = 0%); and the effect of pain relief (OR = 2.68, 95% CI [1.30, 5.55], P  = 0.008), with low heterogeneity ( P  = 0.72, I2 = 0%). Low-to-moderate evidence shows that cinobufotalin injection combined with chemotherapy can significantly increase ORR, DCR, QOL, and the effect of pain relief. Meanwhile, cinobufotalin injection did not bring additional adverse events such as hematological toxicity, gastrointestinal toxicity, cardiotoxicity, hepatotoxicity, and nephrotoxicity; however, multicenter, large-sample, high-quality clinical research results are still needed to reveal the therapeutic effect of cinobufotalin injection in small-cell lung cancer (PROSPERO registration number: CRD42020170052).

Background. Lung metastasis of malignant tumor signifies worse prognosis and immensely deteriorates patients’ life quality. Spatholobi Caulis (SC) has been reported to reduce lung metastasis, but the mechanism remains elusive. Methods. The active components and corresponding targets of SC were obtained from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) database and the SwissTargetPrediction database. The disease targets were acquired from DisGeNET and GeneCards databases. Venn map was composed to figure out intersection targets by using R. The PPI network was constructed through STRING and Cytoscape, and MCODE plug-in was used to sift hub targets. Gene Ontology (GO)-Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was carried out by utilizing clusterProfiler package (R3.6.1) with adjusted P value <0.05. Network of SC-active components-intersection targets-KEGG pathway was accomplished with Cytoscape. Molecular docking between hub targets and active components was performed, analyzed, and visualized by AutoDockTools, AutoDock Vina, PLIP Web tool, and PYMOL. Results. 24 active components and 123 corresponding targets were screened, and the number of disease targets and intersection targets was 1074 and 47, respectively. RELA, JUN, MAPK1, MAPK14, STAT3, IL-4, ESR1, and TP53 were the 8 hub targets. GO analysis and KEGG analysis elucidated that SC could ameliorate lung metastasis mainly by intervening oxidative stress, AGE-RAGE signaling pathway, and microRNAs in cancer. All 8 hub targets were proven to combine successfully with active components of SC. Conclusion. Inflammation is the core factor that integrates all these targets, biological process, and signaling pathways, which indicates that SC prevents or reduces lung metastasis mainly by dispelling inflammation.

Objective:The aim of this meta-analysis was to summarize the available results of immunotherapy predictors for small cell lung cancer (SCLC) and to provide evidence-based information for their potential predictive value of efficacy. Methods: We searched PubMed, EMBASE, Web of Science, The Cochrane Library, and ClinicalTrials (from January 1, 1975 to November 1, 2021). The hazard ratios (HR) and its 95% confidence intervals (CIs) and tumor response rate of the included studies were extracted. Results: Eleven studies were eventually included and the pooled results showed that programmed cell death ligand 1 (PD-L1) positive: objective response rate (ORR) (relative risk [RR] = 1.39, 95% CI [0.48, 4.03], p = 0.54), with high heterogeneity (p = 0.05, I 2 = 56%); disease control rate [DCR] (RR = 1.31, 95% CI [0.04, 38.57], p = 0.88), with high heterogeneity (p = 0.04, I 2 = 75%); overall survival (OS) (HR = 0.89, 95% CI [0.74, 1.07], p = 0.22); and progression-free survival (PFS) (HR = 0.83, 95% CI [0.59, 1.16], p = 0.27), with high heterogeneity (p = 0.005, I 2 = 73.1%). TMB-High (TMB-H): OS (HR = 0.86, 95% CI [0.74, 1.00], p = 0.05); PFS (HR = 0.71, 95% CI [0.6, 0.85], p < 0.001). Lactate dehydrogenase (LDH) >upper limit of normal (ULN): OS (HR = 0.95, 95% CI [0.81, 1.11], p = 0.511). Asian patients: OS (HR = 0.87, 95% CI [0.72, 1.04], p = 0.135); White/ Non-Asian patients: OS (HR = 0.83, 95% CI [0.76, 0.90], p < 0.001). Liver metastasis patients: OS (HR = 0.93, 95% CI [0.83, 1.05], p = 0.229); PFS (HR = 0.84, 95% CI [0.67, 1.06], p = 0.141). Central nervous system (CNS) metastasis patients: OS (HR = 0.91, 95% CI [0.71, 1.17], p = 0.474); PFS (HR = 1.03, 95% CI [0.66, 1.60], p = 0.903). Conclusion:The available research results do not support the recommendation of PD-L1 positive and TMB-H as predictors for the application of immune checkpoint inhibitors (ICIs) in SCLC patients. LDH, baseline liver metastasis and CNS metastasis may be used as markers/influencing factors for predicting the efficacy

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