Lei Yang

TFF3 sensitizes cervical carcinoma cells to cisplatin toxicity by binding to IGF2R

Cisplatin resistance causes ineffectiveness of cisplatin-based treatment for cervical carcinoma. The combination of cisplatin and other chemotherapeutic drugs is an available strategy to overcome this problem. However, chemotherapeutic drugs combined with cisplatin may show tissue toxicity and systemic side effects. Thus, there is a great need of seeking effective substitutes for these chemotherapeutic drugs to improve combination therapy. Here, we found that inactivating IL-6/JAK2/STAT3 signaling pathway sensitized carcinoma cells to cisplatin toxicity by increasing cisplatin accumulation, impairing DNA damage repair, and inhibiting the initiation and development of autophagy, which subsequently caused the increases in DNA damage levels and apoptosis rates in cisplatin-treated cells. We predicted that TFF3 negatively regulated transduction in the IL-6/JAK2/STAT3 pathway based on in silico analysis of the differentially expressed genes (DEGs) between highly trefoil factor 3(TFF3)-encoding mRNA-expressing carcinoma tissues and low-expressing counterparts, and experimentally determined that both ectopic expression of TFF3-encoding gene and TFF3 administration inhibited IL-6-induced STAT3 activation in carcinoma cells. Mechanistically, upon binding to IGF2R, TFF3 stabilized IGF2R by inhibiting the ubiquitin-proteasome degradation pathway to inactivate Akt and thereby STAT3. Moreover, we discovered that TFF3 administration antagonized protective effects of IL-6 stimulation against tumor-killing capacity of cisplatin. Based on these findings, we consider that TFF3 may be employed as a cisplatin sensitizer and have advantages over traditional chemotherapeutic drugs in cisplatin-based combination therapy, since it is a naturally occurring protein in cervical tissue.

A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer

As one of the most common gynecologic malignant tumors, ovarian cancer is usually diagnosed at an advanced and incurable stage because of its early asymptomatic onset. Increasing research into tumor biology has demonstrated that abnormal cellular metabolism precedes tumorigenesis, therefore it has become an area of active research in academia. Cellular metabolism is of great significance in cancer diagnostic and prognostic studies. In this study, we integrated The Cancer Genome Atlas dataset with multiple Gene Expression Omnibus ovarian cancer datasets, identified 17 metabolic pathways with prognostic values using the random forest algorithm, constructed a metabolic risk scoring model based on metabolic pathway enrichment scores, and classified patients with ovarian cancer into two subtypes. Then, we systematically investigated the differences between different subtypes in terms of prognosis, differential gene expression, immune signature enrichment, Hallmark signature enrichment, and somatic mutations. As well, we successfully predicted differences in sensitivity to immunotherapy and chemotherapy drugs in patients with different metabolic risk subtypes. Moreover, we identified 5 drug targets associated with high metabolic risk and low metabolic risk ovarian cancer phenotypes through the weighted correlation network analysis and investigated their roles in the genesis of ovarian cancer. Finally, we developed an XGBoost classifier for predicting metabolic risk types in patients with ovarian cancer, producing a good predictive effect. In light of the above study, the research findings will provide valuable information for prognostic prediction and personalized medical treatment of patients with ovarian cancer.

Stratification of ovarian cancer patients from the prospect of drug target-related transcription factor protein activity: the prognostic and genomic landscape analyses

Abstract The expression and activity of transcription factors, which directly mediate gene transcription, are strictly regulated to control numerous normal cellular processes. In cancer, transcription factor activity is often dysregulated, resulting in abnormal expression of genes related to tumorigenesis and development. The carcinogenicity of transcription factors can be reduced through targeted therapy. However, most studies on the pathogenic and drug-resistant mechanisms of ovarian cancer have focused on the expression and signaling pathways of individual transcription factors. To improve the prognosis and treatment of patients with ovarian cancer, multiple transcription factors should be evaluated simultaneously to determine the effects of their protein activity on drug therapies. In this study, the transcription factor activity of ovarian cancer samples was inferred from virtual inference of protein activity by enriched regulon algorithm using mRNA expression data. Patients were clustered according to their transcription factor protein activities to investigate the association of transcription factor activities of different subtypes with prognosis and drug sensitivity for filtering subtype-specific drugs. Meanwhile, master regulator analysis was utilized to identify master regulators of differential protein activity between clustering subtypes, thereby identifying transcription factors associated with prognosis and assessing their potential as therapeutic targets. Master regulator risk scores were then constructed for guiding patients’ clinical treatment, providing new insights into the treatment of ovarian cancer at the level of transcriptional regulation.