CGB5 (Chorionic Gonadotropin Subunit Beta 5)

This study developed a prognostic model for STAD using the expression levels of MMRGs. Our findings provide insights that could inform future targeted therapeutic approaches.

In addition, the protein expression levels of CGB5, MATN3, MARCKS and APOD in GC tissues were significantly higher than those in normal tissues, and correlated with the pathological characteristics of GC patients. The risk model composed of 7 hub genes can accurately evaluate the prognosis of GC patients, which may contribute to the precise and personalized treatment of GC patients.

CGB5 is significantly up-regulated in various malignancies and strongly correlates with cancer patient prognoses, specifically in malignancies like GC and PAAD. Overall, these findings indicate CGB5 as a promising biomarker for pan-cancer diagnosis and prognosis.

Multiple CGB genes are associated with failure to respond to immune checkpoint inhibitor (ICI) therapy, and CGB7 is particularly strongly predictive of poor prognosis. Overall, our findings indicate that beta-hCG is a clinically accessible, predictive biomarker of immunotherapeutic response.

Overall, the findings suggest that CGB5 may play a crucial role in the development of GC carcinogenesis. Thus, this research may contribute to design of potential drug targets for treatment of GC.

In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Subsequent analyses revealed that the RS signature is independent and has broad applicability among various GC subtypes. In conclusion, we used multi-omics data to obtain five immune-related genes comprising the RS signature, which can independently and effectively predict the prognosis of GC patients with high accuracy.

Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA.

These corresponding prognostic models aid in predicting the prognosis of STAD GlnMgs and immune cell infiltration in the tumor microenvironment (TME) may be possible therapeutic targets in STAD. Furthermore, the glutamine metabolism gene signature presents a credible alternative for predicting STAD outcomes, suggesting that these GlnMgs could open a new field of study for STAD-focused therapy Additional trials are needed to validate the results of the current study.

Finally, we preliminarily verified the role of CGB5, the hub gene in the smoker index, through pan-cancer analysis and in vitro assays. The study digs into the underlying connection between tobacco smoking and the prognosis of bladder cancer from a multi-omics perspective.

In this study, an ICD-based classification was conducted to assist in the diagnosis and personalized therapy for GC. The ICD-related genes risk score model was established to predict prognosis.