A three‑lncRNA signature for prognosis prediction of acute myeloid leukemia in patients

Abstract

Long non-coding RNAs (lncRNAs) are transcripts characterized by >200 nucleotides, without validated protein production. Previous studies have demonstrated that certain lncRNAs have a critical role in the initiation and development of acute myeloid leukemia (AML). In the present study, the subtype-specific lncRNAs in AML was identified. Following the exclusion of the subtype-specific lncRNAs, the prognostic value of lncRNAs was investigated and a three-lncRNA expression-based risk score [long intergenic non-protein coding RNA 926, family with sequence similarity 30 member A and LRRC75A antisense RNA 1 (LRRC75A-AS1)] was developed for AML patient prognosis prediction by analyzing the RNA-seq data of AML patients from Therapeutically Available Research to Generate Effective Treatments (TARGET) and The Cancer Genome Atlas (TCGA) projects. In the training set obtained from TARGET, patients were divided into poor and favorable prognosis groups by the median risk score. The prognostic effectiveness of this lncRNA risk score was confirmed in the validation set obtained from TCGA by the same cut-off. Furthermore, the lncRNA risk score was identified as an independent prognostic factor in the multivariate analysis. As further verification of the independent prognostic power of the lncRNA risk score, stratified analysis was performed by a cytogenetics risk group and revealed a consistent result. The prognostic predictive ability of the risk score was compared with the cytogenetics risk group by time-dependent receiver operating characteristic curves analysis. It was revealed that the combination of the lncRNA risk score and cytogenetics risk group provided a higher prognostic value than a single prognostic factor. The present study also performed co-expression analysis to predict the potential regulatory mechanisms of these lncRNAs in a cis/trans/competing endogenous RNA manner. The results suggested that LRRC75A-AS1 was highly associated with the target genes of transcription factors tumor protein 53 and ETS variant 6. Overall, these results highlighted the use of the three-lncRNA expression-based risk score as a potential molecular biomarker to predict the prognosis in AML patients.

Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology

“…There was no disease information at all available in Open Targets for the top gene, SNHG29. However, under the alias LRRC75A-AS1 this gene is part of a three-lncRNA signature that has been identified for the prognosis prediction of acute myeloid leukemia (AML) patients 24 . The four other genes had some reported association with leukemia.…”

Construction of a 14‑lncRNA risk score system predicting survival of children with acute myelocytic leukemia

“…All available RNA-sequencing data of AML samples were searched using The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) on March 17, 2018. In total, 187 AML samples with RNA-sequencing data were obtained from TCGA (16,17). According to the age information of the samples, 111 samples from patients under the age of 14 years were included.…”

The long non‐coding RNA LOC285758 promotes invasion of acute myeloid leukemia cells by down‐regulating miR‐204‐5p

“…Transfection lncRNA LOC285758 overexpression vector (LOC285758; lnc6180620115353-1-5), negative control (NC; lnc6N0000 002-1-10), miR-204-5p mimics (Mimic; miR10022693- [1][2][3][4][5] and Mimic control (miR01102-1-1) were purchased from RIBOBIO (Guangzhou, China). RNase-free H 2 O (ST876; Beyotime, Shanghai, China) was used to dilute LOC285758, NC, Mimic and Mimic control to 20 lM and was stored at À20°C for later use.…”

“…Our findings suggest that targeting of miR-204-5p by LOC285758 promotes the cell viability and invasion of AML cells, and thus LOC285758 may have potential as a therapeutic target for AML treatment.Acute myeloid leukemia (AML) is the second most common type of leukemia worldwide [1,2]. The latest statistics revealed that the cure rate of AML in the young is 35%, whereas in the aged it is 15% by modern treatment strategies [3]. AML, which is a highly heterogeneous hematopoietic malignancy with an annual incidence of approximately 3/100 000, remains a great challenge in clinical oncology [3,4].…”

“…The latest statistics revealed that the cure rate of AML in the young is 35%, whereas in the aged it is 15% by modern treatment strategies [3]. AML, which is a highly heterogeneous hematopoietic malignancy with an annual incidence of approximately 3/100 000, remains a great challenge in clinical oncology [3,4]. The heterogeneity is also reflected in the lack of knowledge about detailed mechanisms of tumor formation and progression, resulting in less accurate classification and increased difficulty of deciding the appropriate treatment plan [5].…”