Integrative Methylome and Transcriptome Characterization Identifies SERINC2 as a Tumor-Driven Gene for Papillary Thyroid Carcinoma.
Tianxing YingXumeng WangYunjin YaoJimeng YuanShitu ChenLiping WenZhijian ChenXiaofeng WangChi LuoJinghao ShengWeibin WangLisong TengPublished in: Cancers (2022)
Most papillary thyroid carcinomas (PTCs) can be diagnosed preoperatively by routine evaluation, such as thyroid ultrasonography and fine-needle aspiration biopsy. Nevertheless, understanding how to differentiate indolent thyroid tumors from aggressive thyroid cancers remains a challenge, which may cause overtreatment. This study aimed to identify papillary thyroid cancer-specific indicators with whole-genome DNA methylation and gene expression profiles utilizing Infinium Methylation EPIC BeadChip (850k) and RNA arrays. In this paper, we report SERINC2 as a potential tumor-driven indicator in PTC. The up-regulated expression levels of SERINC2 were verified in PTC cell lines via qPCR. Then, cell counting kit 8 (CCK-8), wound healing, and flow cytometric assays were performed to confirm the influence of SERINC2 on proliferation and apoptosis in PTC cell lines after intervention or overexpression. Moreover, the investigation of data from the Cancer Dependency Map (DepMap) provided a potential pathway targeted by SERINC2. The activation of the tryptophan metabolic pathway may reduce the dependency of SERINC2 in thyroid cancers. In conclusion, our results demonstrate the whole-genome DNA methylation and gene expression profiles of papillary thyroid carcinoma, identify SERINC2 as a potential tumor-driven biomarker, and preliminarily verify its function in PTC.
Keyphrases
- genome wide
- dna methylation
- papillary thyroid
- lymph node metastasis
- fine needle aspiration
- copy number
- ultrasound guided
- gene expression
- lymph node
- single cell
- squamous cell carcinoma
- randomized controlled trial
- oxidative stress
- magnetic resonance imaging
- human health
- endoplasmic reticulum stress
- genome wide identification
- risk assessment
- big data
- climate change
- high grade
- cancer therapy
- electronic health record
- clinical practice
- high throughput
- cell therapy
- hodgkin lymphoma
- deep learning