ITPKA (Inositol-Trisphosphate 3-Kinase A)

RRSM is a potential prognostic predictor for STS and lays a foundation for early intervention of high-risk STS patients. The expression of genes FZD7, ITPKA, and PRKAG1 may guide STS treatment decisions.

Cadonilimab combined with taxane and cisplatin as first-line treatment revealed encouraging antitumor activity and manageable safety in patients with advanced ESCC. DNA methylation level might be a potential biomarker for guiding patient outcomes.

We developed and validated a robust nomogram for predicting RFS in cT1 ccRCC patients after nephrectomy, offering valuable insights for individualized patient management.

The nomogram prognostic prediction model, consisting of ITPKA, PDIA2, SAA2, SHOX2, TREML3P, and ZIC2, is capable of predicting the prognosis of ccRCC patients. Among them, SAA2 promotes the proliferation, migration, and invasion of ccRCC cells.

This, in turn, involved significantly in oncogenic function of ITPKA in glioblastoma. Consequently, ITPKA holds promise as a potential target in prospective glioma therapy.

Our study has yielded a novel prognostic model of chemokine-related genes based on comprehensive transcriptional atlas of ccRCC patients, shedding light on the significant impact of the tumor microenvironment on biology and immunotherapy response of ccRCC. We identified IGF2BP3 as a pivotal regulator in regulating ccRCC resistance to sunitinib.

Small molecule compounds targeting the IP3 kinase activity of ITPKA protein may only inhibit the migration and invasion of cancer cells caused by the enhanced ITPKA kinase activity under ATP stimulation, but not the cytoskeletal remodeling caused by the binding of ITPKA protein to F-actin and the driven migration and invasion of cancer cells. Therefore, targeted therapeutic strategy focusing on blocking the binding of ITPKA to F-actin is indispensable when designing the inhibitors targeting ITPKA protein.

Our study has successfully identified significant DEGs between high- and low-staging groups of ccRCC using bioinformatics methods. The construction of a prognostic risk model based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 has displayed promising prognostic significance, indicating its valuable potential for clinical application.