Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis.
Martín Salamini-MontemurriMónica Lamas-MaceirasLidia Lorenzo-CatoiraÁngel Vizoso-VázquezAida Barreiro-AlonsoEsther Rodríguez-BelmonteMaría Quindós-VarelaMaría Esperanza CerdánPublished in: International journal of molecular sciences (2023)
Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.
Keyphrases
- genome wide identification
- systematic review
- genome wide
- long non coding rna
- transcription factor
- genome wide analysis
- meta analyses
- case control
- small cell lung cancer
- network analysis
- poor prognosis
- end stage renal disease
- ejection fraction
- prognostic factors
- copy number
- single cell
- dna methylation
- climate change
- long noncoding rna
- current status
- young adults
- papillary thyroid
- deep learning
- human health
- lymph node metastasis
- patient reported outcomes
- artificial intelligence
- squamous cell