Risk predictive model based on three immune-related gene pairs to assess prognosis and therapeutic sensitivity for hepatocellular carcinoma

Peripheral blood CD8 + CD28+ T cells as predictive biomarkers for treatment response in metastatic colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a substantial global health burden, with treatment outcomes significantly influenced by the interaction between the immune system and the tumor microenvironment.

OBJECTIVE

This study aims to investigate the role of peripheral blood immune cell subpopulations, particularly CD8+ CD28+ T cells, in predicting treatment response in metastatic CRC patients receiving bevacizumab combined with chemotherapy.

METHODS

A cohort of 45 CRC patients was analyzed. Flow cytometry was utilized to assess immune cell subpopulations.

RESULTS

Higher CD8+ CD28+ T cell counts were associated with better treatment responses, including improved objective response rates. In a murine CRC model, the combination therapy significantly inhibited tumor growth and enhanced immune cell function.

CONCLUSION

These findings highlight the importance of CD8+ CD28+ T cells as potential biomarkers for predicting treatment outcomes in CRC. They also suggest that bevacizumab, when combined with chemotherapy, can modulate immune function and improve clinical efficacy.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.