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. 2021 May 20;22(3):bbaa191.
doi: 10.1093/bib/bbaa191.

The microRNA target site landscape is a novel molecular feature associating alternative polyadenylation with immune evasion activity in breast cancer

Soyeon Kim  1 , YuLong Bai  2 , Zhenjiang Fan  3 , Brenda Diergaarde  4 , George C Tseng  5 , Hyun Jung Park  4
Affiliations

The microRNA target site landscape is a novel molecular feature associating alternative polyadenylation with immune evasion activity in breast cancer

Soyeon Kim et al. Brief Bioinform. .

Abstract

Alternative polyadenylation (APA) in breast tumor samples results in the removal/addition of cis-regulatory elements such as microRNA (miRNA) target sites in the 3'-untranslated region (3'-UTRs) of genes. Although previous computational APA studies focused on a subset of genes strongly affected by APA (APA genes), we identify miRNAs of which widespread APA events collectively increase or decrease the number of target sites [probabilistic inference of microRNA target site modification through APA (PRIMATA-APA)]. Using PRIMATA-APA on the cancer genome atlas (TCGA) breast cancer data, we found that the global APA events change the number of the target sites of particular microRNAs [target sites modified miRNA (tamoMiRNA)] enriched for cancer development and treatments. We also found that when knockdown (KD) of NUDT21 in HeLa cells induces a different set of widespread 3'-UTR shortening than TCGA breast cancer data, it changes the target sites of the common tamoMiRNAs. Since the NUDT21 KD experiment previously demonstrated the tumorigenic role of APA events in a miRNA dependent fashion, this result suggests that the APA-initiated tumorigenesis is attributable to the miRNA target site changes, not the APA events themselves. Further, we found that the miRNA target site changes identify tumor cell proliferation and immune cell infiltration to the tumor microenvironment better than the miRNA expression levels or the APA events themselves. Altogether, our computational analyses provide a proof-of-concept demonstration that the miRNA target site information indicates the effect of global APA events with a potential as predictive biomarker.

Keywords: cancer; microRNA; posttranscriptional regulation; tumor heterogeneity.

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Figures

Figure 1
Figure 1
Collective impact of strong and significant APA events. A. Statistical significance of APA genes in a breast tumor-normal sample pair (TCGA-BH-A1FJ) with their ∆PDUI (percentage of PDUI) values (tumor-normal). Since PDUI represents the ratio of isoforms with dUTR, negative ∆PDUI value represent 3′US target genes and positive ∆PDUI value 3′UL genes. Strong APA target genes are in red, significant but not strong ones in pink and not significant ones in gray. B. For 106 breast tumor-normal sample pairs sorted by the number of significant APA target sites, upper panel shows the total number of significant APA genes and the lower panel shows the ratio of the APA genes by whether it is significant but not strong (orange) or strong (red). Black dotted line represents the average ratio of strong APA genes.
Figure 2
Figure 2
Tumor-specific APA-derived miRNA target site changes. A. The heatmap shows tumor-normal samples (row) where the total number of target sites for each miRNA (column) is increased (blue) or decreased (red) due to APA. Not significant changes or no changes are not colored. Samples are sorted by the number of increased miRNA target site modification. B. The total number of miRNA target site changes, either increased (blue) or decreased (red) due to APA, in breast tumor-normal samples pair sorted by the target site number changes per sample pair. C. Number of miRNAs of which target sites are increased (y-axis) or decreased (x-axis) in each tumor-normal sample. The red dotted line represents linear least-squares regression.
Figure 3
Figure 3
APA modifies miRNA target sites associated with cancer. A. The number of tumor-normal samples between which target sites for each miRNA are increased (x-axis) or decreased (y-axis). For further analyses, we dichotomize miRNAs by the amount of target site changes into tamo- (red) and the other (gray) miRNAs (Supplementary Table 1). B. Number of cancer-related miRNAs in tamo- (red) and the other (gray) miRNAs. C. The distribution of phyloP conservation score for 202 tamo- and 191 the other miRNAs.
Figure 4
Figure 4
TamoMiRNAs effectively regulate biological processes. A. Cancer-associated pathways enriched for 99 tamoMiRNAs with their enrichment P-values (red for ‘signaling’, blue for ‘GF’ and green for ‘circadian’). B. Number of target sites for tamoMiRs and the other miRNAs in the genes with more than five target sites. C. Expression fold change (log2 tumor versus normal) of 911 genes that are targets of tamoMiRs and other miRNAs.
Figure 5
Figure 5
KD of NUDT21, an upstream regulator of global 3′US leading to tumorigenesis, induces a different set of APA genes that changes target sites of the common miRNAs. A. Statistical significance of APA genes in NUDT21 KD experiment data with their ∆PDUI values (tumor-normal). Overlap of B. 3′US genes and C. tamoMiRNAs between TCGA breast cancer data and NUDT21 KD data based on PRIMATA-APA. D. Number of cancer-related miRNAs in 191 tamo- (red) and the other (gray) 192 miRNAs. E. The distribution of phyloP conservation score for 139 tamo- and the other 134 miRNAs.
Figure 6
Figure 6
The miRNA target landscape distinguishes tumor samples of different immune activity. A total of 70 TCGA breast tumors ranked by the followings. A. The changes in the decrease (red) or increase (blue) of oncogenic miRNAs. B. The average of normalized miRNA expression of oncogenic miRNAs. C. The total number of APA genes removing/adding target sites of oncogenic miRNAs (bottom panel). The distribution of proliferation signature score of the 25 tumor samples in the left/right-most of the rank (top panel). D. The changes in the decrease (red) or increase (blue) of immune miRNAs. E. The average of normalized miRNA expression of immune miRNAs. F. The total number of APA genes removing/adding target sites of immune miRNAs (bottom panel). The distribution of immune signature score of the 25 tumor samples in the left/right-most of the rank (top panel). Test statistics and the P-values are based on t-test for two independent samples.
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References

    1. Chen M, Lyu G, Han M, et al. . 3′ UTR lengthening as a novel mechanism in regulating cellular senescence. Genome Res 2018;28:285–94. - PMC - PubMed
    1. Dimri GP, Lee X, Basile G, et al. . A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc Natl Acad Sci U S A 1995;92:9363–7. - PMC - PubMed
    1. Busuttil RA, Rubio M, Dollé MET, et al. . Oxygen accelerates the accumulation of mutations during the senescence and immortalization of murine cells in culture. Aging Cell 2003;2:287–94. - PubMed
    1. López-Otín C, Blasco MA, Partridge L, et al. . The hallmarks of aging. Cell 2013;153. - PMC - PubMed
    1. Xia Z, Donehower LA, Cooper TA, et al. . Dynamic analyses of alternative polyadenylation from RNA- seq reveal landscape of 3′ UTR usage across 7 tumor types. Nat Commun 2014;1–38. - PMC - PubMed

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