Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25520
Title: Pan-Cancer Analysis Identifies MNX1 and Associated Antisense Transcripts as Biomarkers for Cancer
Authors: Ragusa, D
Tosi, S
Sisu, C
Keywords: transcription;pan-cancer;MNX1;biomarker;diagnosis;prognosis;cancer cell development
Issue Date: 11-Nov-2022
Publisher: MDPI AG
Citation: Ragusa, D., Tosi, S. and Sisu, C. (2022) 'Pan-Cancer Analysis Identifies MNX1 and Associated Antisense Transcripts as Biomarkers for Cancer', Cells, 11 (22), 3577, pp. 1 - 21. doi: 10.3390/cells11223577.
Abstract: Copyright: © 2022 by the authors. The identification of diagnostic and prognostic biomarkers is a major objective in improving clinical outcomes in cancer, which has been facilitated by the availability of high-throughput gene expression data. A growing interest in non-coding genomic regions has identified dysregulation of long non-coding RNAs (lncRNAs) in several malignancies, suggesting a potential use as biomarkers. In this study, we leveraged data from large-scale sequencing projects to uncover the expression patterns of the MNX1 gene and its associated lncRNAs MNX1-AS1 and MNX1-AS2 in solid tumours. Despite many reports describing MNX1 overexpression in several cancers, limited studies exist on MNX1-AS1 and MNX1-AS2 and their potential as biomarkers. By employing clustering methods to visualise multi-gene relationships, we identified a discriminative power of the three genes in distinguishing tumour vs. normal samples in several cancers of the gastrointestinal tract and reproductive systems, as well as in discerning oesophageal and testicular cancer histological subtypes. Notably, the expressions of MNX1 and its antisenses also correlated with clinical features and endpoints, uncovering previously unreported associations. This work highlights the advantages of using combinatory expression patterns of non-coding transcripts of differentially expressed genes as clinical evaluators and identifies MNX1, MNX1-AS1, and MNX1-AS2 expressions as robust candidate biomarkers for clinical applications
Description: Data Availability Statement: Clinical phenotype and expression data is publicly available and freely accessible in the TCGA-TARGET-GTEX cohort from the University of California Santa Cruz public repository Xena accessible online at www.xenabrowser.net (last accessed on 20 October 2022) [22].
Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells11223577/s1, Figure S1: Hierarchical clustering on Pearson correlation values of expression between MNX1-MNX1-AS1, MNX1-MNX1-AS2, and MNX1-AS1-MNX1-AS2 in each cancer type analysed; Figure S2: Complete hierarchical clustering of MNX1, MNX1-AS1 and MNX1-AS2 expression levels, with cancer type and site information; Figure S3: Site-specific t-SNE plots for discriminating disease subtypes based on MNX1, MNX1-AS1, and MNX1-AS2 expressions; Table S1: Statistically significant correlations between expression levels of MNX1, MNX1-AS1, and MNX1-AS2 and clinicopathological features; Table S2: P values of survival analysis by Kaplan-Meier analysis based on high and low individual expressions of MNX1, MNX1-AS1 and MNX1-AS2, as well as combinatorial (“COMB”); Table S3: Evaluation of the sample data bias impact on the biological insights.
URI: https://bura.brunel.ac.uk/handle/2438/25520
DOI: https://doi.org/10.3390/cells11223577
Other Identifiers: ORCID iDs: Sabrina Tosi https://orcid.org/0000-0002-0036-0191; Cristina Sisu https://orcid.org/0000-0001-9371-0797
3577
Appears in Collections:Dept of Life Sciences Research Papers

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