PI3K/AKT/mTOR pathway-derived risk score exhibits correlation with immune infiltration in uveal melanoma patients

Uncovering the Role of Anoikis-Related Genes in Modulating Immune Infiltration and Pathogenesis of Diabetic Kidney Disease

Background

Diabetic kidney disease (DKD) is an intricate complication of diabetes with limited treatment options. Anoikis, a programmed cell death activated by loss of cell anchorage to the extracellular matrix, participated in various physiological and pathological processes. Our study aimed to elucidate the role of anoikis-related genes in DKD pathogenesis.

Methods

Differentially expressed genes (DEGs) associated with anoikis in DKD were identified. Weighted gene co-expression network analysis (WGCNA) was conducted to identify DKD-correlated modules and assess their functional implications. A diagnostic model for DKD was developed using LASSO regression and Gene set variation analysis (GSVA) was performed for enrichment analysis. Experimental validation was employed to validate the significance of selected genes in the progression of DKD.

Results

We identified 10 anoikis-related DEGs involved in key signaling pathways impacting DKD progression. WGCNA highlighted the yellow module's significant enrichment in immune response and regulatory pathways. Correlation analysis further revealed the association between immune infiltration and anoikis-related DEGs. Our LASSO regression-based diagnostic model demonstrated a well-predictive efficacy with seven identified genes. GSVA indicated that gene function in the high-risk group was primarily associated with immune regulation. Further experimental validation using diabetic mouse models and data analysis in the single-cell dataset confirmed the significance of PYCARD and SFN in DKD progression. High glucose stimulation in RAW264.7 and TCMK-1 cells showed significantly increased expression levels of both Pycard and Sfn. Co-expression analysis demonstrated distinct functions of PYCARD and SFN, with KEGG pathway analysis showing significant enrichment in immune regulation and cell proliferation pathway.

Conclusion

In conclusion, our study provides valuable insights into the molecular mechanisms involved in DKD pathogenesis, specifically highlighting the role of anoikis-related genes in modulating immune infiltration. These findings suggest that targeting these genes may hold promise for future diagnostic and therapeutic approaches in DKD management.

K33 only mutant ubiquitin augments bone marrow-derived dendritic cell-mediated CTL priming via PI3K-Akt pathway

To explore the effect of K33 only mutant ubiquitin (K33O) on bone marrow-derived dendritic cells' (BMDCs') maturity, antigen uptake capability, surface molecule expressions and BMDC-mediated CTL priming, and further investigate the role of PI3K-Akt engaged in K33O-increased BMDC maturation, antigen uptake and presentation, surface molecule expressions and BMDC-based CTL priming. BMDCs were conferred K33O and other ubiquitin mutants (K33R, K48R, K63R-mutant ubiquitin) incubation or LY294002 and wortmannin pretreatment. PI3K-Akt phosphorylation, antigen uptake, antigenic presentation and CD86/MHC class I expression in BMDC were determined by western blot or flow cytometry. BMDC-based CTL proliferation and priming were determined by in vitro mixed lymphocyte reaction (MLR), ex vivo enzyme-linked immunospot assay (Elispot) and flow cytometry with intracellular staining, respectively. The treatment with K33O effectively augmented PI3K-Akt phosphorylation, BMDCs' antigen uptake, antigenic presentation, CD86/MHC class I and CD11c expressions. MLR, Elispot and flow cytometry revealed that K33O treatment obviously enhanced CTL proliferation, CTL priming and perforin/granzyme B expression. The pretreatment with PI3K-Akt inhibitors efficiently abrogated K33O's effects on BMDC. The replenishment of K33 only mutant ubiquitin augments BMDC-mediated CTL priming in bone marrow-derived dendritic cells via PI3K-Akt signalling.

Machine Learning Methods for Gene Selection in Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Transcriptome data-based status of PI3K/AKT/mTOR pathway indicates heterogeneity and immune modulation in patients with pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with limited treatment options. The PI3K/AKT/mTOR pathway is commonly activated in PDAC and plays a critical role in its progression.

METHODS AND RESULTS

In this study, the effect of taselisib (a selective PI3K inhibitor) on PDAC cell proliferation was investigated, and a significant decrease in viability was observed with increasing concentrations of taselisib. Differential analysis on samples from the Genotype-Tissue Expression and The Cancer Genome Atlas databases revealed 24 dysregulated PI3K/AKT/mTOR pathway-related genes (PRGs). Unsupervised clustering-based analysis of transcriptome cohorts revealed two clusters with high consistency between RNA-seq and microarray cohorts. Cluster B had higher enrichment of immune cells, particularly CD8+ T cells, and lower levels of immunosuppressive Treg cells. Moreover, we investigated the relationship between drug sensitivity and different clusters and found that cluster A had a better response to PI3K/AKT/mTOR pathway-related inhibitors and chemotherapy. Finally, cluster A exhibited significant activation of PI3K/AKT/mTOR and related oncogenic pathways, contributing to poor prognosis. The study also developed a risk score based on the expression profiles of PRGs and machine learning, which showed a significant increase in overall survival time among patients in the low-risk group. Importantly, the PI3K/AKT/mTOR pathway could be used to better predict individual risk scores, as evidenced by stratified survival analysis.

CONCLUSIONS

These findings suggest that targeting the PI3K/AKT/mTOR pathway may have therapeutic potential in PDAC, and distinct pathway states, immune modulation and tumor microenvironments have prognostic value.

Next-generation sequencing of uveal melanoma with clinical and histological correlations: Prognostic value of new mutations in the PI3K/AKT/mTOR pathway

BACKGROUND

Uveal melanoma (UM) is the eye's most common primary malignancy and there are no effective therapies for disseminated disease. It is important to try to know the patient's prognosis. The aim of this study was to reflect genetic variants, studied using NGS, of a series of 69 cases of UM and its correlation with histopathology and clinical progression.

METHODS

We performed targeted NGS using a 519-gene panel.

RESULTS

There were selected 28 different mutated genes, showing a total of 231 genetic variants that affected the function of the protein. The most common secondary mutations occurred in SF3B1 (in 26%), followed by BAP1 (in 23%), LRP1B (22%) and FGFR4 (20%). BAP1 mutation was associated with a greater likelihood of metastases and with greater presence of epithelioid cells. LRP1B was also associated with presence of epithelioid cells SF3B1 mutation was significantly associated with a spindle morphology. We found variants in the RAD51B, TOP2A, PTPRD, TSC2, DHX9, PDK1 and MTOR that have not been previously reported in consulted databases. The presence of a mutation in: CHEK2, DHX9 and PDK1 was associated with metastases.

CONCLUSIONS

BAP1 is the most solid biomarker of a poor prognosis in UM and mutations can be detected using NGS. SF3B1 is associated with the spindle cell subtype of UM, which gives it probably a favourable prognostic value. Our study suggests that mutations in DHX9 and PDK1 can have prognostic value. These potential biomarkers are related to the PI3K/AKT/mTOR pathway and makes them candidates for developing new directed therapies.