Research progress on the therapeutic effect and mechanism of metformin for lung cancer (Review).

Lung cancer is the most common type of cancer and the leading cause of cancer­associated death worldwide. Despite the availability of various treatments such as surgery, chemoradiotherapy, targeted drugs and immunotherapy, treatment is expensive and the prognosis remains poor. At present, lung cancer drugs and treatment programs remain in a state of continuous exploration and research to improve the prognosis, and to reduce the pain and economic burden for the patients. Type 2 diabetes is a common chronic disease in middle­aged and elderly patients, leading to significantly increased complications of cardiovascular and cerebrovascular diseases. Epidemiology shows that type 2 diabetes also increases the incidence of malignant tumors, including lung, liver, colorectal and pancreatic cancer. Metformin is a biguanide, widely used as a first­line oral drug in treating type 2 diabetes. Metformin has a hypoglycemic effect and a biological antitumor impact, reducing the incidence of various tumors, including lung cancer, and improving the prognosis of patients with tumors. The anti­lung cancer effect of metformin involves a variety of mechanisms that can improve the therapeutic effect and prognosis of lung cancer, as a single drug or in combination with other therapies. The present study aims to review the associated literature and the therapeutic effects of metformin on lung cancer.

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma.

Background: Lung adenocarcinoma is the most common lung cancer subtype and accounts for the highest proportion of cancer-related deaths. The tumor microenvironment influences prognostic outcomes in lung adenocarcinoma (LUAD). Materials and Methods: We used the ESTIMATE algorithm (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) to investigate the role of microenvironment-related genes and stromal cells in lung adenocarcinoma prognosis. This analysis was done on lung adenocarcinoma cases from The Cancer Genome Atlas (TCGA). The cases were divided into high and low groups on the basis of immune and stromal scores, respectively. Results: There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes. Results: There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes. Results. Based on the enrichment levels of the immune cell types, we clustered LUAD into Immunity_H and Immunity_L subtypes. Most of these genes were upregulated in Immunity_H subtype. Finally, using the Human Protein Atlas (HPA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, most of the proteins corresponding to prognostic genes were verified to be differentially expressed between the tumor and normal groups. Conclusions: The key genes identified in this study are involved in molecular mechanisms of LUAD.

Monitoring methylation-driven genes as prognostic biomarkers in patients with lung squamous cell cancer.

Aberrant DNA methylations have been reported to be significantly associated with lung squamous cell cancer (LUSC). The aim of this study was to investigate the DNA methylation-driven genes in LUSC by integrative bioinformatics analysis. In the present study, methylation-driven genes in LUSC were screened out, and survival analysis related to these genes was performed to confirm their value in prognostic assessment. Gene expression and methylation data were downloaded from The Cancer Genome Atlas (TCGA), and the MethylMix algorithm was used to identify methylation-driven genes. ConsensusPathDB was used to perform Gene Ontology and pathway enrichment analysis of methylation-driven genes. Survival analysis was performed to investigate the correlation with prognosis. In total, 52 differentially expressed methylation-driven genes were identified in LUSC and adjacent tissues. Survival analysis showed that DQX1, GPR75, STX12, and TRIM61 could serve as independent prognostic biomarkers. In addition, the combined methylation and gene expression survival analysis revealed that the combined expression level of the genes ALG1L, DQX1, and ZNF418 alone can be used as a prognostic marker or drug target. Methylation of four sites of gene ZNF418, four sites of ZNF701, two sites of DQX1, and four sites of DCAF4L2 was significantly associated with survival. The present study provides an important bioinformatic and relevant theoretical basis for subsequent early diagnosis and prognostic assessment of LUSC.