Pan-Cancer Analysis Identifies MNX1 and Associated Antisense Transcripts as Biomarkers for Cancer

• Published in: Cells (Basel, Switzerland) Switzerland), 2022-11, Vol.11 (22), p.3577
• Main Authors: Ragusa, Denise, Tosi, Sabrina, Sisu, Cristina
• Format: Article
• Language: English
• Subjects: Antisense RNA; biomarker; Biomarkers; Cancer; Cell Movement - genetics; Cell Proliferation - genetics; Clinical outcomes; Datasets; diagnosis; Gastrointestinal tract; Gene expression; Genomes; Homeodomain Proteins - genetics; Homeodomain Proteins - metabolism; Humans; Male; Malignancy; MNX1; Non-coding RNA; Ontology; pan-cancer; prognosis; Prostate; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; Software; Solid tumors; Statistical significance; Survival analysis; Testicular cancer; Testicular Neoplasms; transcription; Transcription factors; Transcription Factors - genetics; Transcription Factors - metabolism; Tumors
• ISSN: 2073-4409; 2073-4409
• DOI: 10.3390/cells11223577

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 gene and its associated lncRNAs - and - in solid tumours. Despite many reports describing overexpression in several cancers, limited studies exist on - and - 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 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 , - , and - expressions as robust candidate biomarkers for clinical applications.