Identification of the differential expression of genes and upstream microRNAs in small cell lung cancer compared with normal lung based on bioinformatics analysis
Authors: Li Xiuwei , Ma Chao , Luo Huan , Zhang Jian , Wang Jinan , Guo Hongtao 
Published: March 2020

Small cell lung cancer (SCLC) is one of the most lethal cancer, mainly attributing to its high tendency to metastasis. Mounting evidence has demonstrated that genes and microRNAs (miRNAs) are related to human cancer onset and progression including invasion and metastasis.

An eligible gene dataset and an eligible miRNA dataset were downloaded from the Gene Expression Omnibus (GEO) database based our screening criteria. Differentially expressed genes (DE-genes) or DE-miRNAs for each dataset obtained by the R software package. The potential target genes of the top 10 DE-miRNAs were predicted by multiple databases. For annotation, visualization and integrated discovery, Metascape 3.0 was introduced to perform enrichment analysis for the DE-genes and the predicted target genes of the selected top 10 DE-miRNAs, including Pathway and Process Enrichment Analysis or protein–protein interaction enrichment analysis. The intersection of predicted target genes and DE-genes was taken as the final DE-genes. Then apply the predicted miRNAs-targets relationship of top 10 DE-miRNAs to the final DE-genes to gain more convinced DE-miRNAs, DE-genes and their one to one relationship.

GSE19945 (miRNA microarray) and GSE40275 (gene microarray) datasets were selected and downloaded. 56 DE-miRNAs and 861 DE-genes were discovered. 297 miRNAs-targets relationships (284 unique genes) were predicted as the target of top 10 upregulating DE-miRNAs. 245 miRNAs-targets relationships (238 unique genes) were identified as the target of top 10 downregulating DE-miRNAs. The key results of enrichment analysis include protein kinase B signaling, transmembrane receptor protein tyrosine kinase signaling pathway, negative regulation of cell differentiation, response to growth factor, cellular response to lipid, muscle structure development, response to growth factor, signaling by Receptor Tyrosine Kinases, epithelial cell migration, cellular response to organic cyclic compound, Cell Cycle (Mitotic), DNA conformation change, cell division, DNA replication, cell cycle phase transition, blood vessel development, inflammatory response, Staphylococcus aureus infection, leukocyte migration, and myeloid leukocyte activation. Differential expression of genes-upstream miRNAs (RBMS3-hsa-miR-7–5p, NEDD9-hsa-miR-18a-5p, CRIM1-hsa-miR-18a-5p, TGFBR2-hsa-miR-9–5p, MYO1C-hsa-miR-9–5p, KLF4-hsa-miR-7–5p, EMP2-hsa-miR-1290, TMEM2-hsa-miR-18a-5p, CTGF-hsa-miR-18a-5p, TNFAIP3-hsa-miR-18a-5p, THBS1-hsa-miR-182–5p, KPNA2-hsa-miR-144–3p, GPR137C-hsa-miR-1–3p, GRIK3-hsa-miR-144–3p, and MTHFD2-hsa-miR-30a-3p) were identified in SCLC.

RBMS3, NEDD9, CRIM1, KPNA2, GPR137C, GRIK3, hsa-miR-7–5p, hsa-miR-18a-5p, hsa-miR-144–3p, hsa-miR-1–3p along with the pathways included protein kinase B signaling, muscle structure development, Cell Cycle (Mitotic) and blood vessel development may gain a high chance to play a key role in the prognosis of SCLC, but more studies should be conducted to reveal it more clearly.