• 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br Abbreviations TCGA The Cancer Genome Atlas LncRNAs long


    Abbreviations: TCGA, The Cancer Genome Atlas; LncRNAs, long noncoding RNAs; miRNAs, micro RNAs; AJCC, American Joint Committee on Cancer; TNM, tumor-node-metastases; FC, fold changes; MCODE, Molecular Complex Detection; KEGG, Kyoto Encyclopedia of Genes and Genomes; OS, overall survival; PPI, Protein-protein; MAPK, interaction mitogen-activated protein kinase; AMPK, adenosine 5′-monophosphate (AMP)-activated protein kinase
    Corresponding authors at: Department of General Surgery, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai 200040, China.
    E-mail addresses: [email protected] (L. Shi), [email protected] (J. Du). 1 These authors contributed equally to this work.
    development of diseases.
    MiRNAs are 22 nucleotides in length and regulate gene expression post-transcription. Aberrant expression of miRNAs has been associated with tumor suppressor or oncogene activities in various malignancies (Hou et al., 2017). As increasing number of studies have demonstrated the regulatory effect of miRNAs in different steps of carcinogenesis including maturation (Winter et al., 2009), proliferation (Shenoy and Blelloch, 2014), migration, invasion (Shenoy and Blelloch, 2014), au-tophagy (Shenoy and Blelloch, 2014), apoptosis (Otsuka and Ochiya, 2014), and metastasis (Bartel, 2004; Guttman and Rinn, 2012; Munding et al., 2012). Hence, the medical Solasodine has considerable ex-pectations from the application of miRNAs as prospective markers for diagnostic, prognostic, and individualized targeted therapeutic ap-proaches. Despite reports on the functions of miRNAs and their me-chanisms of action in GC, specific miRNA protein-coding gene inter-actions in GC require detailed investigation. Furthermore, although the clinical predictive value of many miRNAs has been recognized, the results of most of these studies are inconsistent, which may have re-sulted from differences in the methods of data processing, platforms of detection, heterogeneous historical sub-types, and small sample size.
    Unfortunately, prognosis after gastrectomy is not always satisfac-tory, presenting a high possibility of local relapse or distant metastases (Orditura et al., 2014). Therefore, an effective prognostic predictive model for GC is required. The American Joint Committee on Cancer's (AJCC's) tumor-node-metastases (TNM) system has been widely used for classifying GC. Currently, Solasodine nomograms have been constructed for various cancers (International Bladder Cancer Nomogram et al., 2006; Karakiewicz et al., 2007; Wierda et al., 2007), which is more ad-vantageous than the existing staging (Mariani et al., 2005; Sternberg, 2006).
    The purpose of the present study was to investigate and examine the differentially expressed miRNAs between normal and cancerous gastric tissues; the gene expression network, interactions between miRNAs and mRNAs, and the biological pathways associated with GC occurrence and development were investigated for developing multi-targeted pre-ventive and therapeutic approaches for GC. In addition, the role of differentially expressed miRNAs in disease prognosis was evaluated, and a signature of five miRNAs was constructed to effectively predict the survival performance of patients with GC. A nomogram was de-veloped for GC prognosis on the basis of the sequencing date collected from 364 patients with GC who had undergone gastrectomy.
    2. Methods
    2.1. Data processing
    TCGA database ( was the source of the raw clinical information and sequencing data used in this study. Based on the selection criteria that samples with both clinical in-formation and sequencing data of miRNAs, and with prognostic in-formation could be included, 364 samples were selected in this study, which included 307 GC samples and 57 matched normal samples. The R language package was adopted and applied for sequencing data pro-cessing. The limma package was used to analyze the differential ex-pression of miRNAs in selected sample tissues. Calculation of the fold changes (FCs) of individual miRNA expression was conducted and dif-ferentially expressed miRNAs with log2 FC > 2.0 were selected. MiRNAs, which were significantly up- and downregulated (identified from sequencing data), were analyzed.