Datasets for the effects of RUNX2 silencing on transcriptomic and metabolomic profiles in SJSA-1 osteosarcoma cells

Osteosarcoma is the most common primary malignant bone tumor with a high risk of metastasis and recurrence. Metabolic reprogramming is a hallmark of osteosarcoma and other cancers and is associated with genetic and epigenetic alterations. RUNX2 is an important transcription factor for osteoblastic differentiation, and aberrant expression of the gene contributes to the development and progression of osteosarcoma. To identify the effects of RUNX2 silencing on transcriptomic and metabolomic profiles in osteosarcomas, we generated SJSA-1 osteosarcoma cells stably expressing RUNX2 shRNA and SJSA-1 cells stably expressing scramble shRNA and analyzed transcriptome and metabolome profiles in the two cell types using Illumina NovaSeq 6000 and ultrahigh-performance liquid chromatography coupled with time-of-flight mass spectrometry, respectively. The datasets can be used by researchers to identify novel targets of RUNX2 and elucidate the role and underlying mechanism of RUNX2 in osteosarcoma pathogenesis and metabolic reprogramming.

a b s t r a c t Osteosarcoma is the most common primary malignant bone tumor with a high risk of metastasis and recurrence.Metabolic reprogramming is a hallmark of osteosarcoma and other cancers and is associated with genetic and epigenetic alterations.RUNX2 is an important transcription factor for osteoblastic differentiation, and aberrant expression of the gene contributes to the development and progression of osteosarcoma.To identify the effects of RUNX2 silencing on transcriptomic and metabolomic profiles in osteosarcomas, we generated SJSA-1 osteosarcoma cells stably expressing RUNX2 shRNA and SJSA-1 cells stably expressing scramble shRNA and analyzed transcriptome and metabolome profiles in the two cell types using Illumina NovaSeq 60 0 0 and ultrahigh-performance liquid chromatography coupled with time-of-flight mass spectrometry, respectively.The datasets can be used by researchers to identify novel targets of RUNX2 and elucidate the role and underlying mechanism of RUNX2 in osteosarcoma pathogenesis and metabolic reprogramming.

Value of the Data
• The datasets present the effects of RUNX2 silencing on the gene expression pattern and metabolite profile in SJSA-1 osteosarcoma cells.• The transcriptome dataset can be used by researchers to identify novel targets of RUNX2 and elucidate the molecular mechanism of RUNX2 in osteosarcoma tumorigenesis.• The metabolome dataset can be used by researchers to elucidate the role of RUNX2 in metabolic reprogramming in osteosarcoma.• The transcriptome and metabolome may be useful for integrated analysis to reveal the link between gene expression alteration and metabolic reprogramming.

Objective
Runt-related transcription factor 2 (RUNX2) is considered a crucial transcriptional regulator of skeletal development [1] .There is increasing evidence that abnormal expression of RUNX2 is a driving factor in osteosarcoma oncogenesis [2 , 3] .However, the molecular mechanism of RUNX2 remains unclear.The objective for the generation of these RNA-Seq and metabolome datasets was to identify the effects of RUNX2 silencing on the gene expression pattern and metabolite profile in osteosarcoma cells and provide raw data.The datasets will be useful for researchers to uncover the role and underlying mechanism of RUNX2 in osteosarcoma.
Metabolome data were deposited in the CNGBdb database [6] with the identifier CNP0 0 04506.Multidimensional statistical analysis, including principal component analysis  (PCA), partial least squares discrimination analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA), showed that the overall metabolites displayed significant differences between the two groups ( Fig. 2 ).Permutation tests showed that the PL S-DA and OPL S-DA models had good reliability and predictability ( Table 3 ).The variable importance for projection (VIP) value of the OPLS-DA model and p value were used to screen differentially expressed metabolites (VIP value > 1 and p value < 0.05).Heatmaps show the results of hierarchical clustering of metabolites with significant differences between the two groups ( Figs. 3 and 4 ).

Table 3
Permutation test parameters for PLS-DA and OPLS-DA models.

Positive-ion mode
Negative-ion mode

RNA Isolation, Library Construction, Sequencing, and Data Analysis
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA).The sequencing library was prepared using the NEBNext® Ultra TM RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) according to the manufacturer's instructions.Briefly, the mRNA was enriched with oligo(dT) beads.Fragmentation buffer was then added to randomly break the mRNA.The first strand of cDNA was synthesized with random hexamer primers, and then the second strand was synthesized with buffer, dNTPs, and DNA polymerase I.The purified double-stranded cDNA was then end-fixed, A-tailed and linked to the sequencing adapter, and the fragment size was selected using AMPure XP beads (Beckman Coulter, Brea, CA, USA), followed by PCR enrichment to obtain the final cDNA library.The library was subjected to 2 × 150 bp paired-end sequencing on the Illumina NovaSeq 60 0 0 (San Diego, CA, USA).Raw image data were converted to raw sequence reads (sequenced reads) by Illumina's CASAVA software (version 1.8), which we refer to as raw data, and stored as FASTQ (Fq) files.Clean data were obtained by trimming off adaptor sequences and removing low-quality reads.The clean reads were mapped to the Homo sapiens GRCh38.87 whole genome using HISAT2 software [7] .Gene expression was calculated as FPKM using featureCounts [5] .

Metabolite Detection and Analysis
Cells were cultured in 10-cm plates, washed with ice-cold PBS and ice-cold saline (0.9% NaCl solution), and then mixed with a methanol/acetonitrile/water mixture (2:2:1, v/v).Cells were collected in a 1.5 ml centrifuge tube, vortexed for 60 s, sonicated at -20 °C for 2 × 30 min,

Fig. 3 .
Fig.3.Heatmaps show the hierarchical clustering results of metabolites with significant differences between the sh1-1 and shNC groups under positive-ion mode.

Fig. 4 .
Fig.4.Heatmaps show the hierarchical clustering results of metabolites with significant differences between the sh1-1 and shNC groups under negative-ion mode.

Table 1
General overview of samples described in this work.

Table 2
Raw reads, clean reads and Phred quality scores of RNA-seq for all samples.