Decoding the genetic symphony: Profiling protein-coding and long noncoding RNA expression in T-acute lymphoblastic leukemia for clinical insights.
Deepak VermaShruti KapoorSarita KumariVinod ScariaJay SinghMercilena BenjaminSameer BakhshiRachna SethBaibaswata NayakAtul SharmaRaja PramanikJayanth Kumar PSridhar SivasubbuVinod ScariaMohit AroraRajive KumarAnita ChopraPublished in: PNAS nexus (2024)
T-acute lymphoblastic leukemia (T-ALL) is a heterogeneous malignancy characterized by the abnormal proliferation of immature T-cell precursors. Despite advances in immunophenotypic classification, understanding the molecular landscape and its impact on patient prognosis remains challenging. In this study, we conducted comprehensive RNA sequencing in a cohort of 35 patients with T-ALL to unravel the intricate transcriptomic profile. Subsequently, we validated the prognostic relevance of 23 targets, encompassing (i) protein-coding genes- BAALC , HHEX , MEF2C , FAT1 , LYL1 , LMO2 , LYN , and TAL1 ; (ii) epigenetic modifiers- DOT1L , EP300 , EML4 , RAG1 , EZH2 , and KDM6A ; and (iii) long noncoding RNAs (lncRNAs)- XIST , PCAT18 , PCAT14 , LINC00202 , LINC00461 , LINC00648 , ST20 , MEF2C-AS1 , and MALAT1 in an independent cohort of 99 patients with T-ALL. Principal component analysis revealed distinct clusters aligning with immunophenotypic subtypes, providing insights into the molecular heterogeneity of T-ALL. The identified signature genes exhibited associations with clinicopathologic features. Survival analysis uncovered several independent predictors of patient outcomes. Higher expression of MEF2C , BAALC , HHEX , and LYL1 genes emerged as robust indicators of poor overall survival (OS), event-free survival (EFS), and relapse-free survival (RFS). Higher LMO2 expression was correlated with adverse EFS and RFS outcomes. Intriguingly, increased expression of lncRNA ST20 coupled with RAG1 demonstrated a favorable prognostic impact on OS, EFS, and RFS. Conclusively, several hitherto unreported associations of gene expression patterns with clinicopathologic features and prognosis were identified, which may help understand T-ALL's molecular pathogenesis and provide prognostic markers.
Keyphrases
- free survival
- long noncoding rna
- poor prognosis
- long non coding rna
- acute lymphoblastic leukemia
- single cell
- gene expression
- genome wide
- binding protein
- dna methylation
- cell proliferation
- machine learning
- type diabetes
- emergency department
- genome wide identification
- rna seq
- signaling pathway
- deep learning
- adipose tissue
- bioinformatics analysis
- small molecule
- quantum dots
- insulin resistance
- acute myeloid leukemia