Dataset Information

Oncogenes overexpressed in metastatic oral cancers from patients with pain: potential pain mediators released in exosomes

ABSTRACT: Oral cancer patients experience pain at the site of the primary cancer. Patients with metastatic oral cancers report greater pain. Lack of pain identifies patients at low risk of metastasis with sensitivity = 0.94 and negative predictive value = 0.89. In the same cohort, sensitivity and negative predictive value of depth of invasion, currently the best predictor, were 0.95 and 0.92, respectively. Cancer pain is attributed to cancer-derived mediators that sensitize neurons and is associated with increased neuronal density. We hypothesized that pain mediators would be overexpressed in metastatic cancers from patients reporting high pain. We identified 40 genes overexpressed in metastatic cancers from patients reporting high pain (n=5) compared to N0 cancers (n=10) and normal tissue (n=5). The genes are enriched for functions in extracellular matrix organization and angiogenesis. They have oncogenic and neuronal functions and are reported in exosomes. Hierarchical clustering according to expression of neurotrophic and axon guidance genes also separated cancers according to pain and nodal status. Depletion of exosomes from cancer cell line supernatant reduced nociceptive behavior in a paw withdrawal assay, supporting a role for exosomes in cancer pain. The identified genes and exosomes are potential therapeutic targets for stopping cancer and attenuating pain.

ORGANISM(S): Homo sapiens

PROVIDER: GSE156178 | GEO | 2020/09/07

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Objective: Chronic pain is a major problem in patients with spinal cord injury (SCI). According to different studies, approximately 70% of patients with SCI experience persistent pain. Although previous gene expression profiling studies have been conducted in animal models, gene expression profiling study in human whole blood has not been reported yet. The objective of this study was to define the gene expression profile of neuropathic pain in spinal cord injured patients. Materials and Methods: We performed gene expression analysis including 20.000 genes using Affymetrix GeneChip® Primeview™ Human Gene Expression Arrays. For confirmation of expression levels of selected genes, we performed real time polymerase chain reaction. We gathered samples from periferic blood mononuclear cells. Data of twelve patients with intractable neuropathic pain was compared with thirteen patients who don’t have pain. All patients have complete injuries with a level of injury above T5. Results: A total of 16 differentially expressed genes including 9 up-regulated and 7 down-regulated were obtained with a cut-off degree at 4 fold change. EIF1AY, RPS4Y1, DDX3Y, RPL31, ETS1, KLF3, JUN, CYorf15B, and ERAP2 were in up-regulated and HLA-C, XIST, C4BPA, FAM118A, ZNF506, OVOS2, and C1D were in down-regulated group. KEGG pathway database showed that 14.6% of genes were enriched in the nodes of immune system. Real time polymerase chain reaction analyses did not increase to statistically significant levels for confirmation of gene expression analyze results. Conclusions: Considering these data, findings of this first gene expression study in humans with SCI might provide valuable information for future targets for understanding neuropathic pain pathogenesis. The results of gene expression analyses were not confirmed by real time polymerase chain reaction. Studies with larger sample size are needed for confirmation. This is a cross-sectional study with a control group. Patients were divided into two groups according to intractable neuropathic pain existence.There were 12 patients in group A (Patients with pain) and 13 patients in control group.

Dataset Information

Oncogenes overexpressed in metastatic oral cancers from patients with pain: potential pain mediators released in exosomes

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets