BackgroundLung adenocarcinoma (LUAD), largely remains a primary cause of cancer-related death worldwide. The molecular mechanisms in LUAD metastasis have not been completely uncovered.MethodsIn this study, we identified differentially expressed genes (DEGs), miRNAs (DEMs) and lncRNAs (DELs) underlying metastasis of LUAD from The Cancer Genome Atlas database. Intersection mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and co-expression network analysis. In addition, survival analyses of intersection mRNAs were conducted. Finally, intersection mRNAs, miRNAs and lncRNAs were subjected to construct miRNA-mRNA-lncRNA network.ResultsA total of 1015 DEGs, 54 DEMs and 22 DELs were identified in LUAD metastasis and non-metastasis samples. GO and KEGG pathway analysis had proven that the functions of intersection mRNAs were closely related with many important processes in cancer pathogenesis. Among the co-expression interactions network, 22 genes in the co-expression network were over the degree 20. These genes imply that they have connections with many other gene nodes. In addition, 14 target genes (ARHGAP11A, ASPM, HELLS, PRC1, TMPO, ARHGAP30, CD52, IL16, IRF8, P2RY13, PRKCB, PTPRC, SASH3 and TRAF3IP3) were found to be associated with survival in patients with LUAD significantly (log-rank P < 0.05). Two lncRNAs (LOC96610 and ADAM6) acting as ceRNAs were identified based on the miRNA-mRNA-lncRNA network.ConclusionsTaken together, the results may provide a novel perspective to develop a multiple gene diagnostic tool for LUAD prognosis, which might also provide potential biomarkers or therapeutic targets for LUAD.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1732-z) contains supplementary material, which is available to authorized users.

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The incidence and mortality of colorectal cancer (CRC) are increasing year by year. The accurate classification of CRC can realize the purpose of personalized and precise treatment for patients. The tumor microenvironment (TME) plays an important role in the malignant progression and immunotherapy of CRC. An in-depth understanding of the clusters based on the TME is of great significance for the discovery of new therapeutic targets for CRC. We extracted data on CRC, including gene expression profile, DNA methylation array, somatic mutations, clinicopathological information, and copy number variation (CNV), from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) (four datasets—GSE14333, GSE17538, GSE38832, and GSE39582), cBioPortal, and FireBrowse. The MCPcounter was utilized to quantify the abundance of 10 TME cells for CRC samples. Cluster repetitive analysis was based on the Hcluster function of the Pheatmap package in R. The ESTIMATE package was applied to compute immune and stromal scores for CRC patients. PCA analysis was used to remove batch effects among different datasets and transform genome-wide DNA methylation profiling into methylation of tumor-infiltrating lymphocyte (MeTIL). We evaluated the mutation differences of the clusters using MOVICS, DeconstructSigs, and GISTIC packages. As for therapy, TIDE and SubMap analyses were carried out to forecast the immunotherapy response of the clusters, and chemotherapeutic sensibility was estimated based on the pRRophetic package. All results were verified in the TCGA and GEO data. Four immune clusters (ImmClust-CS1, ImmClust-CS2, ImmClust-CS3, and ImmClust-CS4) were identified for CRC. The four ImmClusts exhibited distinct TME compositions, cancer-associated fibroblasts (CAFs), functional orientation, and immune checkpoints. The highest immune, stromal, and MeTIL scores were observed in CS2, in contrast to the lowest scores in CS4. CS1 may respond to immunotherapy, while CS2 may respond to immunotherapy after anti-CAFs. Among the four ImmClusts, the top 15 markers with the highest mutation frequency were acquired, and CS1 had significantly lower CNA on the focal level than other subtypes. In addition, CS1 and CS2 patients had more stable chromosomes than CS3 and CS4. The most sensitive chemotherapeutic agents in these four ImmClusts were also found. IHC results revealed that CD29 stained significantly darker in the cancer samples, indicating that their CD29 was highly expressed in colon cancer. This work revealed the novel clusters based on TME for CRC, which would guide in predicting the prognosis, biological features, and appropriate treatment for patients with CRC.

MicroRNAs play critical roles in the occurrence and progression in various cancers including colorectal cancer. Here, we found that microRNA-30a expression was significantly downregulated in colorectal cancer tissues compared to adjacent noncancerous tissues, and the suppression levels of microRNA-30a were significantly associated with tumor differentiation and lymph node metastasis. We also discovered that the expression level of microRNA-30a was inversely proportional to the invasive potential of several colorectal cancer cell lines. Moreover, overexpression of microRNA-30a in colorectal cancer cells inhibited activity of cell migration and invasion. Luciferase reporter assay confirmed metadherin could be a direct target of microRNA-30a, as the overexpression of microRNA-30a decreased metadherin expression at both the protein and messenger RNA levels. Furthermore, the knockdown of metadherin expression in SW620 significantly decreased cell metastasis and invasion. The upregulation of metadherin at the protein level negatively correlated with the expression of microRNA-30a in colorectal cancer tissues, and this upregulation could partially attenuate the effect induced by microRNA-30a. These findings indicate that microRNA-30a may act as a tumor suppressor in colorectal cancer and that microRNA-30a represses cell migration and invasion by decreasing metadherin, highlighting the therapeutic potential of microRNA-30a and metadherin in colorectal cancer treatment.