SRC2 controls CD4+ T cell activation via stimulating c-Myc-mediated upregulation of amino acid transporter Slc7a5

Steroid receptor coactivators in Treg and Th17 cell biology and function

Steroid receptor coactivators (SRCs) are master regulators of transcription that play key roles in human physiology and pathology. SRCs are particularly important for the regulation of the immune system with major roles in lymphocyte fate determination and function, macrophage activity, regulation of nuclear factor κB (NF-κB) transcriptional activity and other immune system biology. The three members of the p160 SRC family comprise a network of immune-regulatory proteins that can function independently or act in synergy with each other, and compensate for - or moderate - the activity of other SRCs. Recent evidence indicates that the SRCs are key participants in governing numerous aspects of CD4+ T cell biology. Here we review findings that establish the SRCs as essential regulators of regulatory T cells (Tregs) and T helper 17 (Th17) cells, with a focus on their crucial roles in Treg immunity in cancer and Treg-Th17 cell phenotypic plasticity.

Establishment of a lysosome-related prognostic signature in breast cancer to predict immune infiltration and therapy response

Background

Lysosomes are instrumental in intracellular degradation and recycling, with their functional alterations holding significance in tumor growth. Nevertheless, the precise role of lysosome-related genes (LRGs) in breast cancer (BC) remains elucidated. This study aimed to establish a prognostic model for BC based on LRGs.

Methods

Employing The Cancer Genome Atlas (TCGA) BC cohort as a training dataset, this study identified differentially expressed lysosome-related genes (DLRGs) through intersecting LRGs with differential expression genes (DEGs) between tumor and normal samples. A prognostic model of BC was subsequently developed using Cox regression analysis and validated within two Gene Expression Omnibus (GEO) external validation sets. Further analyses explored functional pathways, the immune microenvironment, immunotherapeutic responses, and sensitivity to chemotherapeutic drugs in different risk groups. Additionally, the mRNA and protein expression levels of genes within the risk model were examined by utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. Clinical tissue specimens obtained from patients were gathered to validate the expression of the model genes via Real-Time Polymerase Chain Reaction (RT-PCR).

Results

We developed a risk model of BC based on five specific genes (ATP6AP1, SLC7A5, EPDR1, SDC1, and PIGR). The model was validated for overall survival (OS) in two GEO validation sets (p=0.00034 for GSE20685 and p=0.0095 for GSE58812). In addition, the nomogram incorporating clinical factors showed better predictive performance. Compared to the low-risk group, the high-risk group had a higher level of certain immune cell infiltration, including regulatory T cells (Tregs) and type 2 T helper cells (Th2). The high-risk patients appeared to respond less well to general immunotherapy and chemotherapeutic drugs, according to the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and drug sensitivity scores. The RT-PCR results validated the expression trends of some prognostic-related genes in agreement with the previous differential expression analysis.

Conclusion

Our innovative lysosome-associated signature can predict the prognosis for BC patients, offering insights for guiding subsequent immunotherapeutic and chemotherapeutic interventions. Furthermore, it has the potential to provide a scientific foundation for identifying prospective therapeutic targets.