TGFA expression is associated with poor prognosis and promotes the development of cervical cancer.
Xiaoxuan MaJingying ZhengKang HeLiangjia WangZeyu WangKai WangZunlong LiuZhiqiang SanLijing ZhaoLisheng WangPublished in: Journal of cellular and molecular medicine (2023)
Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are the second most common cancers in women aged 20-39. While HPV screening can help with early detection of cervical cancer, many patients are already in the medium to late stages when they are identified. As a result, searching for novel biomarkers to predict CESC prognosis and propose molecular treatment targets is critical. TGFA is a polypeptide growth factor with a high affinity for the epidermal growth factor receptor. Several studies have shown that TGFA can improve cancer growth and progression, but data on its impact on the occurrence and advancement of CESC is limited. In this study, we used clinical data analysis and bioinformatics techniques to explore the relationship between TGFA and CESC. The results showed that TGFA was highly expressed in cervical cancer tissues and cells. TGFA knockdown can inhibit the proliferation, migration and invasion of cervical cancer cells. In addition, after TGFA knockout, the expression of IL family and MMP family proteins in CESC cell lines was significantly reduced. In conclusion, TGFA plays an important role in the occurrence and development of cervical cancer. Therefore, TGFA may become a new target for cervical cancer treatment.
Keyphrases
- poor prognosis
- squamous cell carcinoma
- epidermal growth factor receptor
- growth factor
- data analysis
- long non coding rna
- risk assessment
- end stage renal disease
- tyrosine kinase
- induced apoptosis
- gene expression
- chronic kidney disease
- newly diagnosed
- ejection fraction
- type diabetes
- adipose tissue
- advanced non small cell lung cancer
- young adults
- papillary thyroid
- polycystic ovary syndrome
- prognostic factors
- metabolic syndrome
- radiation therapy
- oxidative stress
- pregnant women
- locally advanced
- cell proliferation
- machine learning
- lymph node metastasis
- cell cycle arrest
- cell death
- cervical cancer screening
- deep learning
- artificial intelligence
- insulin resistance
- skeletal muscle
- patient reported outcomes
- childhood cancer