ORAI1 channel function is a driver in oral cancer progression and pain

Oral cancer causes pain associated with cancer progression. The mechanism(s) underlying the pain is not fully understood. We report here that the function of the Ca2+ channel ORAI1 is an important regulator of oral cancer pain. ORAI1 was highly expressed in tumor samples from oral cancer patients and ORAI1 activation caused sustained Ca2+ influx in human oral cancer cells. RNA-seq analysis showed broad modulation of oral cancer markers such MMPs and pain modulators by ORAI1. Inoculation of oral cancer cells lacking ORAI1 into mouse paws reduced ectopic tumor growth and allodynia, reducing secreted MMP1 levels and the excitation of trigeminal ganglia (TG) neurons. The stimulation of TG neurons with MMP1 evoked an increase in action potentials. These data demonstrate an important role of ORAI1 in oral cancer progression likely by controlling the expression of MMP1 resulting in increased cancer progression and pain Human oral cancer cell lines (HSC-3) were cultured in Dulbecco’s modified eagle medium (DMEM, Gibco, #11885-084) supplemented with 10% fetal bovine serum (FBS, Gibco, #26140-079) and 1% penicillin-streptomycin (PS, Gibco, #15140-122). Cells were harvested by treating 0.25 % Trypsin/EDTA (Gibco, #25200-072) and subcultured (2-3x105 cells) in 100 mm dishes at 37oC in a humidified 20 % O2/ 5 % CO2 incubator for 3-4 days. Cells were stimulated with 25 µM cyclopiazonic acid (CPA, Sigma, #1530) in the presence/absence of the ORAI inhibitor synta-66 for 3 hours before washing the cells and isolating the mRNA. The Illumina sequencing run produced 774,131,516 raw paired-end reads from 12 samples (54,874,503-71,100,845 reads). Quality control of raw sequencing reads was performed using FastQC (v0.11.5).Low-quality reads, sequencing adapters and overrepresented K-mers were removed using Trimmomatic (v0.32) resulting a total of 708,579,673 trimmed reads (46,791,160-66,701,914). The reads were aligned to the Human reference genome (Ensembl release 84-GRCh38) using the STAR aligner (v2.5.2a) and default alignment parameters to produce BAM files. HTSeq-Count (v0.6.1p1) was then used to generate read counts per gene based on the 84-GRCh38 GTF considering that the RNASeq library is stranded. The counts generated were then filtered to remove unexpressed or very lowly expressed genes to retain only genes with a minimum of 5 reads in 50% of samples in at least one of the three conditions investigated. Differential gene expression analysis was then performed using the filtered count data and the DESeq2 (1.28.1) program. Both unsupervised (PCA and correlation analysis) and supervised analyses were done using DESeq2. Statistical significance was analyzed using the Benjamini-Hochberg FDR (5%) and fold change of 1.5. Both FDR values and fold change were generatedusing DESeq2.Data transformation was performed using variance stabilizing transformation (VST). Heatmaps of levels of gene expression and fold change were constructed using the Ward method implemented in JMP Genomics (SAS Institute).