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| Gene expression analysis and gene alternative splicing (AS) event analysis reveal different transcriptome characteristics associated with colorectal cancer development. (A) Heatmap shows 2,313 differentially expressed genes (A, log2FC >1, adjusted p-value < 0.01) and (B) 338 differentially expressed AS events of 319 genes (difference >0.2, FDR <0.01) between 50 paired normal and tumor tissues. (C) Venn diagram displays the overlapping genes with differentially expressed mRNA levels and AS events between 50 paired normal and tumor tissue. (D) GO analysis of genes with differentially expressed mRNA levels and (E) genes with differentially expressed AS events.
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Background: Gene expression and alternative splicing (AS) can promote cancer development via complex mechanisms. We aimed to identify and verify the hub AS events and splicing factors associated with the progression of colorectal cancer (CRC).
Methods: RNA-Seq data, clinical data, and AS events of 590 CRC samples were obtained from the TCGA and TCG...
Contexts in source publication
Context 1
... comparing differentially expressed genes between 50 paired normal and tumor tissues, 2,313 genes were obtained and displayed by heatmap (Log2FC >1, adjusted p-value < 0.01, Figure 1A and Supplementary Table 4). Through ANOVA analysis, we found 338 AS events of 319 genes that were differentially expressed between normal and tumor tissues (difference >0.2, FDR <0.01, Figure 1B and Supplementary Table 5). ...
Context 2
... comparing differentially expressed genes between 50 paired normal and tumor tissues, 2,313 genes were obtained and displayed by heatmap (Log2FC >1, adjusted p-value < 0.01, Figure 1A and Supplementary Table 4). Through ANOVA analysis, we found 338 AS events of 319 genes that were differentially expressed between normal and tumor tissues (difference >0.2, FDR <0.01, Figure 1B and Supplementary Table 5). Using the Venn diagram, we found that most of the genes with differentially expressed mRNA levels were different from the genes with differentially expressed AS events ( Figure 1C). ...
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... ANOVA analysis, we found 338 AS events of 319 genes that were differentially expressed between normal and tumor tissues (difference >0.2, FDR <0.01, Figure 1B and Supplementary Table 5). Using the Venn diagram, we found that most of the genes with differentially expressed mRNA levels were different from the genes with differentially expressed AS events ( Figure 1C). GO analysis revealed that pathways enriched by genes with differentially expressed mRNA levels ( Figure 1D) were also inconsistent with pathways enriched by genes with differentially expressed AS events (Figure 1E). ...
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... the Venn diagram, we found that most of the genes with differentially expressed mRNA levels were different from the genes with differentially expressed AS events ( Figure 1C). GO analysis revealed that pathways enriched by genes with differentially expressed mRNA levels ( Figure 1D) were also inconsistent with pathways enriched by genes with differentially expressed AS events (Figure 1E). These results indicate that the carcinogenic mechanisms involved in gene expression differed from gene AS events. ...
Context 5
... the Venn diagram, we found that most of the genes with differentially expressed mRNA levels were different from the genes with differentially expressed AS events ( Figure 1C). GO analysis revealed that pathways enriched by genes with differentially expressed mRNA levels ( Figure 1D) were also inconsistent with pathways enriched by genes with differentially expressed AS events (Figure 1E). These results indicate that the carcinogenic mechanisms involved in gene expression differed from gene AS events. ...
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... of the 16 genes with prognostic AS events, the mRNA level of only one gene was related to poor prognosis (ACOXL, Figure 2B), and the mRNA level of only one gene correlated with good prognosis (EPB41L2, Figure 2B). By constructing a coexpression network of gene expression and gene AS events (R > 0.3, p < 0.001), we identified five hub gene AS events: EPB41L2, CELF2, TMEM130, VCL, and SORBS2 ( Figure 2C and Supplementary Table 1). GO analysis revealed that the 300 genes whose mRNA levels were significantly related to the gene AS events were significantly involved in the GO terms ( Figure 2D). ...
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Citations
... hand. The 6 up-regulated genes (IL-20, CLK1, SORBS2, ERG1, PIM1, SNORD3A) are involved in cancer development [43][44][45][46][47][48]. Interleukin 20 (IL-20) is a cytokine assigned to the interleukin 10 family described as tumor promoting [49]. ...
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Background
Alternative splicing (AS) plays a crucial role in transcriptomic diversity and is a hallmark of cancer that profoundly influences the development and progression of prostate cancer (PCa), a prevalent and potentially life-limiting cancer among men. Accumulating evidence has highlighted the association between AS dysregulation and the onset and progression of PCa. However, a comprehensive and integrative analysis of AS profiles at the event level, utilising data from multiple high-throughput cohorts and evaluating the prognosis of PCa progression, remains lacking and calls for thorough exploration.
Results
We identified a differentially expressed retained intron event in ZWINT across three distinct cohorts, encompassing an original array-based dataset profiled by us previously and two RNA sequencing (RNA-seq) datasets. Subsequent in-depth analyses of these RNA-seq datasets revealed 141 altered events, of which 21 demonstrated a significant association with patients’ biochemical recurrence-free survival (BCRFS). We formulated an AS event-based prognostic signature, capturing six pivotal events in genes CYP4F12, NFATC4, PIGO, CYP3A5, ALS2CL, and FXYD3. This signature effectively differentiated high-risk patients diagnosed with PCa, who experienced shorter BCRFS, from their low-risk counterparts. Notably, the signature's predictive power surpassed traditional clinicopathological markers in forecasting 5-year BCRFS, demonstrating robust performance in both internal and external validation sets. Lastly, we constructed a novel nomogram that integrates patients’ Gleason scores with pathological tumour stages, demonstrating improved prognostication of BCRFS.
Conclusions
Prediction of clinical progression remains elusive in PCa. This research uncovers novel splicing events associated with BCRFS, augmenting existing prognostic tools, thus potentially refining clinical decision-making.
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