RNA-seq of vertebral metastatic tumor samples from pan-cancer primary tumors II

To define the molecular underpinning of bone metastasis in pan-cancer, we performed high-throughput transcriptome sequencing (RNA-seq) using total RNA from veterbral metatases of LUAD, BRCA, PRAD, SCRA, LIHC, SKCM, THYM, etc. tissues. Afterwards, expression profile were used for further analysis. Overall design: Total RNA was isolated using MJzol animal RNA Extraction Kit (MagBeads) #T02096 (MJzol, China). Paired-end libraries were synthesized by using the TruSeq® RNA Sample Preparation Kit (Illumina, USA) following TruSeq® RNA Sample Preparation Guide. Briefly, The poly-A containing mRNA molecules were purified using poly-T oligo-attached magnetic beads. Following purification, the mRNA is fragmented into small pieces using divalent cations under 94? for 8 min. The cleaved RNA fragments are copied into first strand cDNA using reverse transcriptase and random primers. This is followed by second strand cDNA synthesis using DNA Polymerase I and RNase H. These cDNA fragments then go through an end repair process, the addition of a single 'A' base, and then ligation of the adapters. The products are then purified and enriched with PCR to create the final cDNA library. Purified libraries were quantified by Qubit® 2.0 Fluorometer (Life Technologies, USA) and validated by Agilent 2100 bioanalyzer (Agilent Technologies, USA) to confirm the insert size and calculate the mole concentration. Cluster was generated by cBot with the library diluted to 10 pM and then were sequenced on the Illumina Noveseq 6000 (Illumina, USA). The library construction and sequencing was performed at Shanghai Biotechnology Corporation. Sequencing raw reads were preprocessed by filtering out rRNA reads, sequencing adapters, short-fragment reads and other low-quality reads. We used Hisat2(version:2.0.4)to map the cleaned reads to the GRCh38 reference genome with two mismatches. After genome mapping, Stringtie(version:1.3.0) was run with a reference annotation to generate FPKM values for known gene models. Differentially expressed genes were identified using Stringtie]. The p-value significance threshold in multiple tests was set by the false discovery rate (FDR) . The fold-changes were also estimated according to the FPKM in each sample.The differentially expressed genes were selected using the following filter criteria: FDR =0.05 and fold-change =2.