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Cited by 3RNA Research and SplicingCancer-related molecular mechanisms researchMicroRNA in disease regulation

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Post-transcriptional control drives Aurora kinase A expression in human cancers

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Cacioppo, Roberta, Rad, Deniz, Pagani, Giulia, Gandellini, Paolo, Lindon, Catherine·2024·PLOS ONE
DOI 10.1371/journal.pone.0310625PMC PMC11554201

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Abstract

Aurora kinase A (AURKA) is a major regulator of the cell cycle. A prominent association exists between high expression of AURKA and cancer, and impairment of AURKA levels can trigger its oncogenic activity. In order to explore the contribution of post-transcriptional regulation to AURKA expression in different cancers, we carried out a meta-analysis of -omics data of 18 cancer types from The Cancer Genome Atlas (TCGA). Our study confirmed a general trend for increased AURKA mRNA in cancer compared to normal tissues and revealed that AURKA expression is highly dependent on post-transcriptional control in several cancers. Correlation and clustering analyses of AURKA mRNA and protein expression, and expression of AURKA-targeting hsa-let-7a miRNA, unveiled that hsa-let-7a is likely involved to varying extents in controlling AURKA expression in cancers. We then measured differences in the short/long ratio (SLR) of the two alternative cleavage and polyadenylation (APA) isoforms of AURKA mRNA across cancers compared to the respective healthy counterparts. We suggest that the interplay between APA and hsa-let-7a targeting of AURKA mRNA may influence AURKA expression in some cancers. hsa-let-7a and APA may also independently contribute to altered AURKA levels. Therefore, we argue that AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation.

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Figures

Fig 1 Pan-cancer analysis of AURKA mRNA and protein expression. (A) Median and 95% CI of AURKA mRNA FPKM values in 18 TCGA cancers compared to their respective normal tissue (NT). Number of datasets per condition shown in Table 1, filtered set. Kruskal-Wallis with Dunnett’s multiple comparisons test. ns, not significant; *, p<0.02; ****, p<0.0001. (B) Distribution of r coefficients of the AURKA mRNA-protein correlation across cancers. p<0.001 for all r coefficients except KIRP, PCPG, PRAD and THCA (p>0.05). (C) Graphic representation of molecular processes likely underlying the AURKA mRNA-protein correlation in cancer samples. Figure created with BioRender.com.
Fig 1 Pan-cancer analysis of AURKA mRNA and protein expression. (A) Median and 95% CI of AURKA mRNA FPKM values in 18 TCGA cancers compared to their respective normal tissue (NT). Number of datasets per condition shown in Table 1, filtered set. Kruskal-Wallis with Dunnett’s multiple comparisons test. ns, not significant; *, p<0.02; ****, p<0.0001. (B) Distribution of r coefficients of the AURKA mRNA-protein correlation across cancers. p<0.001 for all r coefficients except KIRP, PCPG, PRAD and THCA (p>0.05). (C) Graphic representation of molecular processes likely underlying the AURKA mRNA-protein correlation in cancer samples. Figure created with BioRender.com.CC BY · PubMed Central
Fig 2 Pan-cancer analysis of hsa-let-7a expression and involvement in AURKA expression. (A) Median and 95% CI of hsa-let-7a RPM values in 18 TCGA cancers compared to their respective normal tissue (NT). Number of datasets per condition shown in Table 2, filtered set. Kruskal-Wallis with Dunnett’s multiple comparisons test. ns, not significant; **, p<0.01; ***, p<0.001; ****, p<0.0001. (B) Distribution of r coefficients of the AURKA mRNA-hsa-let-7a correlation across cancers. p<0.001 for r coefficients of BRCA, LUAD, PRAD, THCA, UCEC; p<0.05 for r coefficient of LUSC; p>0.05 for all other r coefficients. (C) Distribution of r coefficients of the AURKA protein-hsa-let-7a correlation across cancers. p<0.001 for r coefficients of BRCA and UCEC; p>0.05 for all other r coefficients. (D) Clusters found according to common trends in the two types of correlation. The r coefficient of the indicated correlation is plotted on each axis. Data was also plotted on an interactive 3D graph visible in full interactive mode at math3d.org/bpYNRCnPA. (E) Table showing clusters composition and speculative involvement of hsa-let-7a in controlling AURKA expression.
Fig 2 Pan-cancer analysis of hsa-let-7a expression and involvement in AURKA expression. (A) Median and 95% CI of hsa-let-7a RPM values in 18 TCGA cancers compared to their respective normal tissue (NT). Number of datasets per condition shown in Table 2, filtered set. Kruskal-Wallis with Dunnett’s multiple comparisons test. ns, not significant; **, p<0.01; ***, p<0.001; ****, p<0.0001. (B) Distribution of r coefficients of the AURKA mRNA-hsa-let-7a correlation across cancers. p<0.001 for r coefficients of BRCA, LUAD, PRAD, THCA, UCEC; p<0.05 for r coefficient of LUSC; p>0.05 for all other r coefficients. (C) Distribution of r coefficients of the AURKA protein-hsa-let-7a correlation across cancers. p<0.001 for r coefficients of BRCA and UCEC; p>0.05 for all other r coefficients. (D) Clusters found according to common trends in the two types of correlation. The r coefficient of the indicated correlation is plotted on each axis. Data was also plotted on an interactive 3D graph visible in full interactive mode at math3d.org/bpYNRCnPA. (E) Table showing clusters composition and speculative involvement of hsa-let-7a in controlling AURKA expression.CC BY · PubMed Central
Fig 3 Pan-cancer profiling of AURKA APA mRNA isoforms. (A) Integrative Genomics Viewer (IGV) view of the depth of coverage of reads mapping to the 3’ end of the AURKA-203 transcript (the last exon and 3’ UTR are shown at the bottom, in blue). A subset of BRCA primary tumour samples is shown, selected to represent a range of SLRs, from low (top) to high (bottom). Reference genome GRCh38/hg38. Note, the y-axis scale (read depth) differs across panels. (B), (C) Mean and standard error of the mean of SLR fold change values for cancer-normal sample pairs of AURKA transcript ENST00000371356.6 (B) and CCND1 transcript ENST00000227507.3 (C). Value of the mean indicated. SLR values were calculated using APAtrap and the analysis was performed once. n, number of cancer-normal sample pairs. Y axis in log2 scale.
Fig 3 Pan-cancer profiling of AURKA APA mRNA isoforms. (A) Integrative Genomics Viewer (IGV) view of the depth of coverage of reads mapping to the 3’ end of the AURKA-203 transcript (the last exon and 3’ UTR are shown at the bottom, in blue). A subset of BRCA primary tumour samples is shown, selected to represent a range of SLRs, from low (top) to high (bottom). Reference genome GRCh38/hg38. Note, the y-axis scale (read depth) differs across panels. (B), (C) Mean and standard error of the mean of SLR fold change values for cancer-normal sample pairs of AURKA transcript ENST00000371356.6 (B) and CCND1 transcript ENST00000227507.3 (C). Value of the mean indicated. SLR values were calculated using APAtrap and the analysis was performed once. n, number of cancer-normal sample pairs. Y axis in log2 scale.CC BY · PubMed Central
Fig 4 Interplay of APA and hsa-let-7a in regulating AURKA expression. (A) Distribution of r coefficients of the AURKA protein-SLR correlation across cancers. p<0.05 only for BRCA. (B) Scatter plot showing cancers according to the cancer/normal AURKA SLR fold change and to the value of the ‘protein vs SLR’ correlation coefficient r. (C) Distribution of r coefficients of the AURKA mRNA-SLR correlation across cancers. p>0.05 for all r coefficients. (D) Scatter plot showing cancers according to the r coefficient of the ‘protein vs SLR’ and of the ‘hsa-let-7a vs SLR’ correlations.
Fig 4 Interplay of APA and hsa-let-7a in regulating AURKA expression. (A) Distribution of r coefficients of the AURKA protein-SLR correlation across cancers. p<0.05 only for BRCA. (B) Scatter plot showing cancers according to the cancer/normal AURKA SLR fold change and to the value of the ‘protein vs SLR’ correlation coefficient r. (C) Distribution of r coefficients of the AURKA mRNA-SLR correlation across cancers. p>0.05 for all r coefficients. (D) Scatter plot showing cancers according to the r coefficient of the ‘protein vs SLR’ and of the ‘hsa-let-7a vs SLR’ correlations.CC BY · PubMed Central
Fig 5 Heterogeneity of AURKA splicing isoforms across cancers. (A) UCSC Genome Browser view of AURKA transcript variants annotated in the NCBI (top) and Ensembl (bottom) databases. AURKA gene resides on the negative strand. The MANE isoform identical in both databases is shown in light blue at the bottom. (B) Violin plots showing the expression level [log2(TPM+1)] of the individual Ensembl AURKA transcripts in the TCGA cancers on the y axis. Cancer types in columns, AURKA transcripts in rows. TPM, transcript per million. Figure downloaded from the GEPIA2 platform. (A), (B) ENST00000395907.5 (AURKA-204) transcript is displayed although it has lower annotation quality.
Fig 5 Heterogeneity of AURKA splicing isoforms across cancers. (A) UCSC Genome Browser view of AURKA transcript variants annotated in the NCBI (top) and Ensembl (bottom) databases. AURKA gene resides on the negative strand. The MANE isoform identical in both databases is shown in light blue at the bottom. (B) Violin plots showing the expression level [log2(TPM+1)] of the individual Ensembl AURKA transcripts in the TCGA cancers on the y axis. Cancer types in columns, AURKA transcripts in rows. TPM, transcript per million. Figure downloaded from the GEPIA2 platform. (A), (B) ENST00000395907.5 (AURKA-204) transcript is displayed although it has lower annotation quality.CC BY · PubMed Central

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Metadata

Authors
Cacioppo, R., Rad, D., Pagani, G., Gandellini, P., & Lindon, C.
Affiliations
  • MRC Laboratory of Molecular Biology (GB)
  • University of Cambridge (GB)
  • University of Milan (IT)
Corresponding author
  • Cacioppo, Roberta
  • Lindon, Catherine
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2024-11-11
Volume
19
Issue
11
Pages
e0310625
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0310625
PMC
PMC11554201
OpenAlex
W4404241562
Cited by
3
References
78
Citations by year
2026: 2 · 2025: 1
FWCI
0.12
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Biotechnology and Biological Sciences Research Council (BB/R004137/1)
  • Department of Pharmacology, University of Cambridge
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
Keywords
AURKA, post-transcriptional regulation, alternative polyadenylation, microRNA, cancer, cell cycle regulation, mRNA isoforms
MeSH terms
Humans, Neoplasms, RNA Processing, Post-Transcriptional, RNA, Messenger, Gene Expression Regulation, Neoplastic, Polyadenylation, MicroRNAs, Aurora Kinase A

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CoChat. (2026). Citation check: Post-transcriptional control drives Aurora kinase A expression in human cancers. https://app.cochat.ai/papers/10.1371/journal.pone.0310625
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Cacioppo, R., Rad, D., Pagani, G., Gandellini, P., & Lindon, C. (2024). Post-transcriptional control drives Aurora kinase A expression in human cancers. PLOS ONE. https://doi.org/10.1371/journal.pone.0310625

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