AML-CRNDE-Analysis

Data analysis for LncRNA CRNDE in AML.

Analysis of The Cancer Genome Atlas and beatAML RNA-seq data

The available RNA-seq data of 151 de novo AML samples from TCGA and 249 de novo AML samples from Beat AML cohort were used for this study. RNA-seq reads were aligned to GRCh38 whole genome with Ensembl v84 using Hisat2 2.2.0, StringTie 2.1.2 and Ballgown 2.18.0 were used to assemble the alignments into full and partial transcripts and estimate the expression levels of all lncRNAs. Limma algorithm was used to analyze the differential expression of the lncRNAs.

Interpretation biotype in Ensembl

Defination of LncRNA in Ensembl GTF file:

Processed transcript
3' overlapping ncRNA
Antisense
Non coding
Retained intron
Sense intronic
Sense overlapping
lincRNA

Align to the reference genome

hisat2 -x hisat2_index/genome_snp_tran -1 Input_R1.fq.gz -2 Input_R2.fq.gz \
       --fr --threads 10 --dta 2> Input.summary.txt | samtools view -F 4 - -b | \
       samtools sort -o Input.bam > Input.stdout.log 2> Input.stderr.log

samtools index Input.bam

Transcriptome assembly

stringtie Input.bam -p 10 -G Homo_sapiens.GRCh38.84.gtf -eB -o Input.gtf

Gene quantification

library(ballgown)
bg <- ballgown(dataDir="~/CRNDE/Analyze/", samplePattern="BA", meas='all')
gene_expression = gexpr(bg)
write.table(gene_expression,"AML_Expr.txt", row.names=TRUE, col.names=TRUE, quote=FALSE, sep="\t")

Differential Gene Expression

Genes with expression < 0.1 FPKM in less than 25% samples were removed from further analysis. Batch effects between two cohorts were removed with Combat from sva package. For more details, please see TCGA_BEAT-AML-RNAseq-Analysis.R.

Analysis of the microarray gene expression data

The GSE12662 microarray expression data set was retrieved from the Gene Expression Omnibus (GEO) Database. RMA was used to normalize the raw expression data. The biotype of lncRNAs were annotated based on "EnsDb.Hsapiens.v79" package. Limma was used to discover the differentially expressed lncRNAs between groups. For more details, please see Microarray-Analysis.R.

Session Information

R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 10 (buster)

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] venn_1.9                    sva_3.34.0                  genefilter_1.68.0          
 [4] mgcv_1.8-31                 nlme_3.1-144                limma_3.42.2               
 [7] RColorBrewer_1.1-2          gplots_3.0.4                lattice_0.20-38            
[10] tximport_1.14.2             pheatmap_1.0.12             DESeq2_1.26.0              
[13] SummarizedExperiment_1.16.1 DelayedArray_0.12.3         BiocParallel_1.20.1        
[16] matrixStats_0.56.0          Biobase_2.46.0              GenomicRanges_1.38.0       
[19] GenomeInfoDb_1.22.1         IRanges_2.20.2              S4Vectors_0.24.4           
[22] SRAdb_1.48.2                RCurl_1.98-1.2              graph_1.64.0               
[25] BiocGenerics_0.32.0         RSQLite_2.2.0              

loaded via a namespace (and not attached):
 [1] bitops_1.0-6           bit64_4.0.5            tools_3.6.3            backports_1.1.9       
 [5] R6_2.4.1               KernSmooth_2.23-16     rpart_4.1-15           Hmisc_4.4-1           
 [9] DBI_1.1.0              colorspace_1.4-1       nnet_7.3-12            tidyselect_1.1.0      
[13] gridExtra_2.3          bit_4.0.4              compiler_3.6.3         htmlTable_2.0.1       
[17] xml2_1.3.2             caTools_1.18.0         scales_1.1.1           checkmate_2.0.0       
[21] readr_1.3.1            stringr_1.4.0          digest_0.6.25          foreign_0.8-75        
[25] GEOquery_2.54.1        XVector_0.26.0         base64enc_0.1-3        jpeg_0.1-8.1          
[29] pkgconfig_2.0.3        htmltools_0.5.0        htmlwidgets_1.5.1      rlang_0.4.7           
[33] rstudioapi_0.11        farver_2.0.3           generics_0.0.2         gtools_3.8.2          
[37] dplyr_1.0.2            magrittr_1.5           GenomeInfoDbData_1.2.2 Formula_1.2-3         
[41] Matrix_1.2-18          Rcpp_1.0.5             munsell_0.5.0          lifecycle_0.2.0       
[45] stringi_1.4.6          zlibbioc_1.32.0        grid_3.6.3             blob_1.2.1            
[49] gdata_2.18.0           crayon_1.3.4           splines_3.6.3          annotate_1.64.0       
[53] hms_0.5.3              locfit_1.5-9.4         knitr_1.29             pillar_1.4.6          
[57] admisc_0.8             geneplotter_1.64.0     XML_3.99-0.3           glue_1.4.2            
[61] latticeExtra_0.6-29    data.table_1.13.0      png_0.1-7              vctrs_0.3.4           
[65] gtable_0.3.0           purrr_0.3.4            tidyr_1.1.2            ggplot2_3.3.2         
[69] xfun_0.16              xtable_1.8-4           survival_3.2-3         tibble_3.0.3          
[73] AnnotationDbi_1.48.0   memoise_1.1.0          cluster_2.1.0          ellipsis_0.3.1

Citation

Xuefei Ma, Wei Zhang, Ming Zhao, Shufen Li, Wen Jin & Kankan Wang. Oncogenic role of lncRNA CRNDE in acute promyelocytic leukemia and NPM1-mutant acute myeloid leukemia. Cell Death Discov. 6, 121 (2020). https://doi.org/10.1038/s41420-020-00359-y

rstatistics/AML-CRNDE-Analysis