BACKGROUND: Lung cancer is one of the leading causes of cancer-related mortality worldwide, with above 80% of cases be non-small cell lung cancer (NSCLC), among which lung squamous cell carcinoma (LUSC) occupies a significant proportion. Although comprehensive cancer therapies have considerably improved the overall survival of patients, patients with advanced LUSC have a poorer prognosis. Therefore, there is a need for a biomarker to predict the progress of advanced LUSC in order to improve prognosis through early diagnosis. Previous studies have shown that miRNAs are differentially expressed in lung cancer tissues and play roles as potential oncogenes or tumor suppressors. The aim of this study is to identify differentially expressed miRNAs between early-stage and advanced-stage LUSC, and to establish a set of miRNAs that can predict the progress of advanced LUSC. METHODS: Clinical data and miRNA-related data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Bioinformatic methods were applied to analyze the data. Receiver operating characteristic (ROC) curves were plotted, and various online tools were used to predict target genes, with subsequent analysis of the potential biological mechanisms of these genes. RESULTS: A total of 58 differentially expressed miRNAs were identified between the experiment group and the control group. Seven miRNAs were selected for potential construction of a miRNA biomarker through LASSO regression, and based on the area under the curve (AUC) values of each miRNA, four of these miRNAs (miR-377-3p, miR-4779, miR-6803-5p, miR-3960) were ultimately chosen as biomarkers for predicting advanced LUSC. The AUC under the ROC curve for the combined four miRNAs was 0.865. Enrichment analysis showed that these target genes were involved in several pathways, including cancer-related pathways, mitogen-activated protein kinase (MAPK) signaling pathway, serine/threonine kinase, and tyrosine kinase signaling pathways. CONCLUSIONS The combined use of miR-377-3p, miR-4779, miR-6803-5p and miR-3960 provides a good predictive ability for the progress of advanced LUSC patients, with an AUC of 0.865.
Humans ; MicroRNAs/metabolism* ; Lung Neoplasms/metabolism* ; Biomarkers, Tumor/metabolism* ; Carcinoma, Squamous Cell/pathology* ; Gene Expression Regulation, Neoplastic ; Male ; Female ; Prognosis ; ROC Curve ; Middle Aged

BACKGROUND: Lung cancer represents the main cause of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) is the most main subtype. More than half of NSCLC patients have already developed distant metastasis (DM) at the time of diagnosis and have a poor prognosis. Therefore, it is necessary to find new biomarkers for predicting NSCLC DM in order to guide subsequent treatment and thus improve the prognosis of NSCLC patients. Numerous studies have shown that microRNAs (miRNAs) are abnormally expressed in lung cancer tissues and play an important role in tumorigenesis and progression. The aim of this study is to identify differentially expressed miRNAs in lung adenocarcinoma tissues with DM group compared to those with non-distant metastasis (NDM) group, and to construct a miRNA signature for predicting DM of lung adenocarcinoma. METHODS: We first obtained miRNA and clinical data for patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Subsequently, bioinformatics analysis, which included different R packages, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a range of online analysis tools, was performed to analyze the data. RESULTS A total of 12 differentially expressed miRNAs were identified between the DM and NDM groups, and 8 miRNAs (miR-377-5p, miR-381-5p, miR-490-5p, miR-519d-5p, miR-3136-5p, miR-320e, miR-2355-5p, miR-6784-5p) were screened for constructing a miRNA signature. The efficacy of this miRNA signature in predicting DM was good with an area under the curve (AUC) of 0.831. Logistic regression analysis showed that this miRNA signature was an independent risk factor for DM of lung adenocarcinoma. Next, target genes of the eight miRNAs were predicted, and enrichment analysis showed that these target genes were enriched in a variety of pathways, including pathways in cancer, herpes simplex virus I infection, PI3K-Akt pathway, MAPK pathway, Ras pathway, etc. CONCLUSIONS: This miRNA signature has good efficacy in predicting DM of lung adenocarcinoma and has the potential to be a predictor of DM of lung adenocarcinoma.
Humans ; MicroRNAs/metabolism* ; Lung Neoplasms/diagnosis* ; Male ; Neoplasm Metastasis ; Female ; Gene Expression Regulation, Neoplastic ; Middle Aged ; Prognosis ; Gene Expression Profiling ; Aged ; Biomarkers, Tumor/genetics*