RESUMO
Despite extensive research, the exact mechanisms involved in colorectal cancer (CRC) etiology and pathogenesis remain unclear. This study aimed to examine the correlation between tumor-associated alternative splicing (AS) events and tumor immune infiltration (TII) in CRC. We analyzed transcriptome profiling and clinical CRC data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq and Innate databases, respectively to develop and validate a risk model of differential AS events and subsequently a TII risk model. We then conducted a two-factor survival analysis to study the association between TII and AS risk and evaluated the associations between immune signatures and six types of immune cells based on the TIMER database. Subsequently, we studied the distribution of six types of TII cells in high- and low-risk groups for seven AS events and in total. We obtained the profiles of AS events/genes for 484 patients, which included 473 CRC tumor samples and 41 corresponding normal samples, and detected 22581 AS events in 8122 genes. Exon Skip (ES) (8446) and Mutually Exclusive Exons (ME) (74) exhibited the most and fewest AS events, respectively. We then classified the 433 patients with CRC into low-risk (n = 217) and high-risk (n = 216) groups based on the median risk score in different AS events. Compared with patients with low-risk scores (mortality = 11.8%), patients with high-risk scores were associated with poor overall survival (mortality = 27.6%). The risk score, cancer stage, and pathological stage (T, M, and N) were closely correlated with prognosis in patients with CRC (P < 0.001). We identified 6479 differentially expressed genes from the transcriptome profiles of CRC and intersected 468 differential immune-related signatures. High-AS-risk and high-TII-risk predicted a poor prognosis in CRC. Different AS types were associated with different TII risk characteristics. Alternate Acceptor site (AA) and Alternate Promoter (AP) events directly affected the concentration of CD4T cells, and the level of CD8T cells was closely correlated with Alternate Terminator (AT) and Exon Skip (ES) events. Thus, the concentration of CD4T and CD8T cells in the CRC immune microenvironment was not specifically modulated by AS. However, B cell, dendritic cell, macrophage, and neutrophilic cell levels were strongly correlated with AS events. These results indicate adverse associations between AS event risk levels and immune cell infiltration density. Taken together, our findings show a clear association between tumor-associated alternative splicing and immune cell infiltration events and patient outcome and could form a basis for the identification of novel markers and therapeutic targets for CRC and other cancers in the future.
RESUMO
BACKGROUND: The molecular mechanism of quercetin in the prevention and treatment of AS has been widely reported. However, the microbial and metabolic characteristics of quercetin in AS treatment are still poorly understood. In this study, we aimed to explore the gut microbial and metabolic signatures of quercetin in AS treatment and conduct an integrative analysis on its biomechanism. METHODS: An atherosclerosis mouse model was induced by a high cholesterol diet (HCD). The duration of the quercetin treatment was 12 weeks. We measured TC, TG, HDL and LDL for plasma biochemical analysis and TNF-α and IL-6 for plasma inflammatory analysis. Haematoxylin-eosin (HE) staining was conducted to evaluate the aortic structure and atherosclerosis. Bacterial DNA, which was extracted from mouse faeces, was identified by the V3-V4 regions of the 16S rRNA for microbiological analysis. The HeatMap package of BTtools was applied to visualize the data of the microbial difference matrix according to the OTU results. Fecal metabolites were assessed through LC-MS. Multivariate data analysis was conducted on the normalized data with SIMCA-P+. Significantly different metabolites were extracted based on the Pearson correlation coefficients at the level of P<0.05. Key significantly changed metabolites were screened from the intersection between metabolic signatures of the normal-model and model-quercetin groups. To investigate the biological function of quercetin on AS, we identified the differential metabolic signatures of the model vs. quercetin groups and performed KEGG analyses via MBROLE, MetaboAnalyst database. RESULTS: Quercetin treatment for 12 weeks significantly reduced the levels of TC (P<0.001), TG (P<0.05), HDL (P<0.001), LDL (P<0.001), TNF-α (P<0.001) and IL-6 (P<0.001) compared with the model group. HE staining indicated that quercetin could protect damaged vessels caused by HFD. Bacteroidetes, Firmicutes and Proteobacteria were dominant microbial groups in the samples. There was no significant difference between the three groups (P>0.05) at the phylum level, and the genera Phascolarctobacterium and Anaerovibrio can be regarded as the key microbiota signatures of quercetin treatment. PLS-DA results further showed that these 18 faecal metabolites (clustered in 3 groups) had significant differences between the control, model and quercetin groups throughout the 12-day treatment. According to the quantitative analysis results, 32 key metabolic signatures were screened for quercetin treatment. The main pathway in quercetin treatment is primary bile acid biosynthesis, as 3α,7α,12α,26-tetrahydroxy-5ß-cholestane (C27H48O4) was defined as the most important key metabolic signature. CONCLUSIONS: We explored the gut microbial and metabolic involvement of quercetin in AS treatment and suggest the association between AS and gut metabolic regulation.
