Comparative analysis of transient receptor potential channel 5 opposite strand-induced gene expression patterns and protein-protein interactions in triple-negative breast cancer

Establishment of an immunogenic cell death-related model for prognostic prediction and identification of therapeutic targets in endometrial carcinoma

OBJECTIVE

Studies have firmly established the pivotal role of the immunogenic cell death (ICD) in the development of tumors. This study seeks to develop a risk model related to ICD to predict the prognosis of patients with endometrial carcinoma (EC).

MATERIALS AND METHODS

RNA-seq data of EC retrieved from TCGA database were analyzed using R software. We determined clusters based on ICD-related genes (ICDRGs) expression levels. Cox and LASSO analyses were further used to build the prediction model, and its accuracy was evaluated in the train and validation sets. Finally, in vitro and in vivo experiments were conducted to confirm the impact of the high-risk gene IFNA2 on EC.

RESULTS

Patients were sorted into two ICD clusters, with survival analysis revealing divergent prognoses between the clusters. The Cox regression analysis identified prognostic risk genes, and the LASSO analysis constructed a model based on 9 of these genes. Notably, this model displayed excellent predictive accuracy when validated. Finally, increased IFNA2 levels led to decreased vitality, proliferation, and invasiveness in vitro. IFNA2 also has significant tumor inhibiting effect in vivo.

CONCLUSIONS

The ICD-related model can accurately predict the prognosis of patients with EC, and IFNA2 may be a potential treatment target.

Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

Background

We explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood.

Methods

We integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments.

Results

Analysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness.

Conclusion

SRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients.

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

Background

Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.

Methods

We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.

Results

We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.

Conclusions

The risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.