Construction of lung cancer serum markers based on ReliefF feature selection.

Serum miRNAs are available clinical samples for cancer screening. Identifying early serum markers in lung cancer (LC) is essential for patients' early diagnosis and clinical treatment. Expression data of serum miRNAs of lung adenocarcinoma (LUAD) patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO). These data were normalized and subjected to differential expression analysis to obtain differentially expressed miRNAs (DEmiRNAs). The DEmiRNAs were subsequently subjected to ReliefF feature selection, and subsets closely related to cancer were screened as candidate feature miRNAs. Thereafter, a Gaussian Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifier were constructed based on these candidate feature miRNAs. Then the best diagnostic signature was constructed through NB combined with incremental feature selection (IFS). Thereafter, these samples were subjected to principal component analysis (PCA) based on miRNAs with optimal predictive performance. Finally, the peripheral serum miRNAs of 64 LUAD patients and 59 normal individuals were extracted for qRT-PCR analysis to validate the performance of the diagnostic model in respect of clinical detection. Finally, according to area under the curve (AUC) and accuracy values, the NB classifier composed of miR-5100 and miR-663a manifested the most outstanding diagnostic performance. The PCA results also revealed that the 2-miRNA diagnostic signature could effectively distinguish cancer patients from healthy individuals. Finally, qRT-PCR results of clinical serum samples revealed that miR-5100 and miR-663a expression in tumor samples was remarkably higher than that in normal samples. The AUC of the 2-miRNA diagnostic signature was 0.968. In summary, we identified markers (miR-5100 and miR-663a) in serum for early LUAD screening, providing ideas for developing early LUAD diagnostic models.

SNHG15 enhances cisplatin resistance in lung adenocarcinoma by affecting the DNA repair capacity of cancer cells.

BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent malignancy. SNHG15 has been demonstrated to be oncogenic in many kinds of cancers, however the mechanism of SNHG15 in LUAD cisplatin (DDP) resistance remains unclear. In this study, we demonstrated the effect of SNHG15 on DDP resistance in LUAD and its related mechanism. METHODS: Bioinformatics analysis was adopted to assess SNHG15 expression in LUAD tissues and predict the downstream genes of SNHG15. The binding relationship between SNHG15 and downstream regulatory genes was proved through RNA immunoprecipitation, chromatin immunoprecipitation and dual-luciferase reporter assays. Cell counting kit-8 assay was adopted to evaluate LUAD cell viability, and gene expression was determined by Western blot and quantitative real-time polymerase chain reaction. We then performed comet assay to assess DNA damage. Cell apoptosis was detected by Tunnel assay. Xenograft animal models were created to test the function of SNHG15 in vivo. RESULTS: SNHG15 was up-regulated in LUAD cells. Moreover, SNHG15 was also highly expressed in drug-resistant LUAD cells. Down-regulated SNHG15 strengthened the sensitivity of LUAD cells to DDP and induced DNA damage. SNHG15 could elevate ECE2 expression through binding with E2F1, and it could induce DDP resistance by modulating the E2F1/ECE2 axis. In vivo experiments verified that the SNHG15 could enhance DDP resistance in LUAD tissue. CONCLUSION: The results suggested that SNHG15 could up-regulate ECE2 expression by recruiting E2F1, thereby enhancing the DDP resistance of LUAD.