Differentially expressed gene profiles and associated ceRNA network in ATG7-Deficient lens epithelial cells under oxidative stress

Slit2 Promotes H2O2-Induced Lens Epithelial Cells Oxidative Damage and Age-Related Cataract

PURPOSE

To analyze the role of Slit2 in lens epithelial cell oxidative damage and its underlying mechanism.

METHODS

Human lens epithelial cells (SRA01/04 cells) and rat transparent lens were cultured with H2O2 to establish cell oxidative stress models and rat cataract models. Immunohistochemistry, quantitative real-time polymerase chain reaction (qRT-PCR), and Western blot assays were employed to detect Slit2 levels within age-related cataracts(ARC) lens anterior capsule samples, rat cataract models, and cell oxidative stress models. In this study, qRT-PCR and Western blot assays were performed to derermine E-cadherin, N-cadherin, occludens1(ZO-1), α-SMA(α‑smooth muscle actin), Bcl-2, Bax, p-AKT, and AKT levels. In addition, Flow cytometry were performed to examine reactive oxygen species (ROS) and cell apoptosis. Cell viability, invasion, and migration were detected by CCK8, Transwell, and Wound healing.

RESULTS

Increased expression of Slit2 was found in ARC lens anterior capsule samples, H2O2-induced rat cataract models, and Human lens epithelial cells (HLECs) oxidative stress models. H2O2 significantly increased cell apoptosis and ROS generation, also accelerating cell migration, invasion, and epithelial-mesenchymal transition (EMT). In addition, H2O2 treatment repressed AKT phosphorylation and cell viability. Knock-down of Slit2 promoted cell viability and AKT phosphorylation levels, as well as repressed cell invasion, migration, apoptosis, ROS production and EMT.

CONCLUSION

Slit2 promoted lens epithelial cells oxidative stress damage via the AKT signalling pathways, providing a novel insight in ARC treatment.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.

METHODS

We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).

RESULTS

The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.

CONCLUSIONS

In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.