SRMS expression in CRC.
Initially, we compared mRNA expression levels of SRMS in GEPIA, which matched TCGA normal and GTEx data. mRNA expression levels of SRMS were significantly elevated in CRC tissues than in the paired adjacent normal tissues (p 0.05, Figure 1A). Moreover, from the HPA database, protein expression levels of SRMS were moderate in normal tissues and high elevation in tumor tissues (Figure 1B). Data in the Oncomine database showed that CNVs of SRMS were significantly elevated in colon adenocarcinoma (COAD) tissues than in paired normal tissues (p ˂ 0.01, Figure 1C). Therefore, SRMS expression is a potential diagnostic indicator for CRC.
SRMS expression in clinical characteristic sub-groups.
The association between SRMS and clinicopathological features was further evaluated in the online cancer OMICS database, UALCAN. In subgroup analyses based on age, gender, race, clinical stage, histological and nodal metastasis status, the transcription level of SRMS was significantly elevated in COAD patients than in healthy individuals (p<0.05, Figure 2A-F). Moreover, the higher the expression of SRMS, the higher the clinical stages and nodal metastasis. The expression of SRMS in stage 4 was higher than in stage 1 and 2 (p=0.0028, 0.015, respectively). The expression of SRMS in N2 was high than in N0 (p=0.048).These findings implythat SRMS playsanimportantroleintumorprogression.
SRMS co-expression networks in CRC.
To elucidate on the functional properties of SRMS in CRC, the “LinkFinder” module in LinkedOmics was used to analyze the co-expression networks of SRMS. As shown in the volcano plot (Figure 3A), a total of 2176 genes (red dots) were significantly positively correlated with SRMS while 1368 genes (green dots) were significantly negatively correlated (p<0.05). The heatmaps show the top 50 genes that were positively and negatively correlated with SRMS (Figure 3B-C). SRMS expression was positively correlated with the expression of C20orf195 (positive rank #1, r = 0.389, p = 3.90E-15), HES2 (r = 0.301, p = 2.20E-9), PTK6(r = 0.300, p = 3.04E-9) and SYNGR3 (r = 0.300, p = 2.43E-9) among others. This result suggests that SRMS has various effects on the transcriptome.
Gene Ontology term and KEGG pathway by GSEA showed that co-expressed genes were mainly involved in the intermediate filament-based processes, epidermis development and protein autophosphorylation (Figure 3D). In contrast, translational initiation and elongation, mitochondrial gene expression, and cytoplasmic translation were inhibited. KEGG pathway analysis showed enrichment in inositol phosphate metabolism, ribosomes, proteasomes, oxidative phosphorylation and DNA replication among others. (Figure 3E).
Association between SRMS and immune signatures.
Figure 4 shows that some immune subsets were associated with SRMS mRNA expression levels in COAD and READ. The 7 types of tumor-infiltrating lymphocytes that exhibited important correlations with SRMS in COAD included activated CD4+ T cells (Act CD4; Spearman: r=-0.196, p=2.37e-05), CD56 dim natural killer cells (CD56 dim, Spearman: r=0.283, p=7.68e-10), Memory B cells (MEM B, Spearman: r=-0.115, p=0.0137), Neutrophils (Neutrophil, Spearman: r=0.102, p=0.0293), Effector memory CD4+ T cells (Tem CD4, Spearman: r=-0.296, p=1.17e-10), Type 2 T helper cells (Th2, Spearman: r=-0.187, p=5.98e-05), Type 17 T helper cell (Th17, Spearman: r=0.23, p=6.6e-07).
The 5 types of tumor-infiltrating lymphocytes that exhibited important correlations with SRMS in READ included activated dendritic cells (Act DC, Spearman: r=0.23, p=6.6e-07), CD56 dim natural killer cells (CD56 dim, Spearman: r=0.214, p=0.006), effector memory CD4 T cells (Tem CD4, Spearman: r=-0.188, p=0.011), Type 2 T helper cells (Th2, Spearman: r=-0.187, p=0.0213), Type 17 T helper cells (Th17, Spearman: r=0.178, p=0.021).
Moreover, we identified 33 immunostimulators (C10orf54, CD276, CD28, CD70, CXCL12, ENTPD1, IL6R, KLRK1, RAET1E, TMRM173, TMIGD2, ULBP1, TNFRSF13B, TNFRSF14, TNFRSF18, TNFRSF25, TNFRSF4, TNFSF4 and TNFSF9) and 8 immuno-inhibitors (ADORA2A, BTLA, CD160, KDR, LGALS9, PVRL2, TGFB1 and TGFBR1; Figure 5A) that were significantly associated with SRMS in CRC.
Immune signatures associated with the SRMS gene.
The SRMS-associated 33 immunomodulators and 191 immune cell marker genes were analyzed in the STRING database to validate functional connectivity. From the STRING database, we obtained the SRMS-associated immune gene PPI network (enrichment p-value<1.0e-16) of a total of 214 nodes and 829 edges, which represented proteins and functional interactions (Figure 5B). Next, we performed an enrichment analysis of these genes used GO and KEGG (figure5C). Consequently, we probed the signaling pathways through which SRMS regulates immune responses in CRC. Figure 5D shows that these genes were mainly enriched in chemokine, cancer, IL-17, inflammatory bowel disease (IBD), intestinal immune network for IgA production, and in cytokine-cytokine receptor interaction signaling pathways. These signaling pathways may related to SRMS-mediated immune events.