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  • br The gene expression profile was preprocessed using Limma

    2020-08-12


    The gene expression profile was preprocessed using Limma package in Bioconductor and Affymetrix annotation files. The Background cor-rection, quantile normalization and probe summarization of the mi-croarray data were performed using the Robust Multi-array average algorithm to obtain the gene expression matrix.
    2.5.3. Identification of differential expressed genes (DEGs)
    Limma package was used to normalize the microarray raw data, and genes with (log2fold change) > = 2 and P < 0.01 indicated that there is a statistically significant difference between the cancer tissues and normal controls. A total of 602 up-regulated and 765 down-regulated differentially expressed genes were identified when comparing shSNRPA1 silencing with control shRNA groups.
    2.5.4. Enrichment analysis of DEGs
    The on-line functional annotation tool, DAVID (www.abcc.ncifcrf. gov), was then used to perform the GO-BP functional enrichment ana-lysis for DEGs, with the threshold of P < 0.01. Pathway enrichment analysis was done using both KEGG (www.kegg.jp/Kegg/pathway. html) and Reactome (www.reactome.org) databases, P < 0.01 was selected as the threshold value.
    2.5.5. Identification of genes associated with SNRPA1 from DEGs
    The Tumor Suppressor Gene database (bioinfo.mc.vanderbilt.edu/ TSGene) and the Tumor-associated Gene database (blog.synopse.info/ tag/Database) were used to identify all known oncogenes or cancer suppressor genes from the DEGs, special focus was placed on those genes interacting with SNRPA1 and also involved in inhibiting the proliferation and promoting apoptosis of cancer cells.
    2.5.6. Pathway and network analysis
    STRING version 9.1 (www.string.embl.de) was used to search in-teraction associations of the proteins with the confidence score of > 0.9. Cytoscape software (www.cytoscape.org) was then used for performing visualization of the PPI network. The HUB nodes with the top 5 degrees in the PPI network were obtained. BioNet was used to
    identify the PPI sub-network of DEGs with a false discovery rate of < 0.01. KEGG database was used to perform the pathway enrichment analysis of genes in the core PPI sub-network with a threshold value of P < 0.01.
    The list of significant SNRPA1-dependent genes identified by Affymetrix probe set IDs, fold changes and p values were uploaded into the Ingenuity Pathway Analysis (IPA) tool (www.ingenuity.com). Each 4311-88-0 identifier was mapped to its corresponding gene object in the Ingenuity Pathway Knowledge Base (IPKB). These focus genes were then used for constructing biological networks, using the “IPA” core analysis function. To start building networks, the application queries the IPKB for interactions between focus genes and all other gene 4311-88-0 objects stored in the knowledge base, and generates a set of networks. Every resulting gene interaction has supporting literature findings available online. IPA then computes a score for each network according to the fit of the user’s set of significant genes. The score is derived from p-value and indicates the likelihood of the focus genes in a network being found together as a result of random chance. A score of 2 indicates that there is a 1-in-100 chance that the focus genes are together in a network as a result of random chance. Therefore, scores of 2 or higher has at least 99% confidence of not being generated by random chance alone.
    3. Results & discussion
    3.1. Knocking down of SNRPA1 inhibited the proliferation of RKO and HCT116 cells
    3.1.1. SNRPA1 is widely expressed in a variety of CRC cell lines Previous studies indicated that compared to normal colorectal
    samples, SNRPA1 is highly expressed in a variety of cancers including CRC [9–11]. To validate those findings, we examined the transcrip-tional expression of SNRPA1 in four representative CRC cell lines. The representative results are summarized in Fig. 1A, which shows that SNRPA1 is actively transcribed in the four selected CRC cell lines. In terms of its relative transcriptional level, SNRPA1 expressed most highly in HT29 cells, followed by SW480, while its transcription in RKO and HCT116 is relatively lower than that in SW480 or HT29. The lowest transcriptional expression of SNRPA1 was found in RKO cells.
    3.1.2. Knocking down of SNRPA1 inhibited cell proliferation of RKO and HCT116 cells
    The higher expression of SNRPA1 has been implicated in the CRC and it has also become a prognostic biomarker for many types of can-cers. Except its role in mediating the RNA processing, SNRPA1 has not been annotated with other functions in CRC. Based on the current re-search on SNRPA1, we speculated that SNPRA1 should also play im-portant roles in the progression of CRC. To validate this hypothesis, we designed and produced a shRNA lentivirus to knock down the expres-sion of SNRPA1 and examined the subsequent effects of SNRPA1 si-lencing on the cellular functions of CRC cells. As shown in Fig. 2, the expression of SNRPA1 at both mRNA (Fig. 1B, C) and protein (Fig. 1D) levels in RKO and HCT116 cells were significantly knocked down by anti-SNRPA1 shRNA lentivirus. Compared to HCT 116 cells, it showed that SNRPA1 knocking down effects mediated by shRNA lentivirus is more prominent in RKO cells, reaching ˜ 80% reduction at the mRNA level (Fig. 1C), while that in HCT116 cells was about 70% (Fig. 1B). The relative expression of SNPRA1 proteins in these two cell lines are comparable with their mRNA expression, respectively. Those above results indicated that anti-SNRPA1 shRNA lentivirus has successfully knocked down the expression of SNRPA1 at both mRNA and protein levels in RKO and HCT116 cells. The shRNA lentivirus works better in RKO cells than in HCT116 cells, which may due to different efficiency of shRNA lentivirus in different cell lines.