Overexpression of Raf-1 kinase inhibitor protein inhibits cell invasion and migration in lung cancer cells through suppressing epithelial-mesenchymal transition

Identifying key factors in cell fate decisions by machine learning interpretable strategies

Cell fate decisions and transitions are common in almost all developmental processes. Therefore, it is important to identify the decision-making mechanisms and important individual molecules behind the fate decision processes. In this paper, we propose an interpretable strategy based on systematic perturbation, unsupervised hierarchical cluster analysis (HCA), machine learning (ML), and Shapley additive explanation (SHAP) analysis for inferring the contribution and importance of individual molecules in cell fate decision and transition processes. In order to verify feasibility of the approach, we apply it to the core epithelial to mesenchymal transition (EMT)-metastasis network. The key factors identified in EMT-metastasis are consistent with relevant experimental observations. The approach presented here can be applied to other biological networks to identify important factors related to cell fate decisions and transitions.

NeiyiKangfu tablets control the progression of endometriosis through inhibiting RAF/MEK/ERK signal pathway by targeting RKIP

BACKGROUND

NeiyiKangfu tablets (NYKF) are widely used clinically for the treatment of endometriosis (EMS), whose mechanism of action has been extensively studied. Researchers have found that NYKF may control the development of ectopic lesions by inhibiting angiogenesis and inflammatory cytokine secretion. Nevertheless, NYKF's mechanism of action remains unclear.

METHODS

In the present study, the function of NYKF in the progression of EMS and the associated underlying mechanism was investigated by in vivo and in vitro experiments. EMS model mice were treated with NYKF and the pro-inflammatory factors and apoptosis of ectopic endometrium as well as RAF/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) signaling activation were assessed. In addition, human endometriosis-derived immortalized entopic stromal (hEM15A) cells transfected with or without RAF kinase inhibitor protein (RKIP)-small-interfering RNA (siRNA) were also treated with NYKF and the proliferation, migration, apoptosis, and RAF/MEK/ERK signaling activation were measured by Cell Counting Kit-8 (CCK-8), flow cytometry, Transwell, and western blot, respectively.

RESULTS

Results showed that NYKF increased the expression of RKIP, inhibited RAF/MEK/ERK signaling activation, and induced apoptosis while inhibiting proliferation and migration both in EMS mice and hEM15A cells. RKIP knockdown could inhibit the effect of NYKF treatment, leading to the activation of RAF/MEK/ERK signaling and the proliferation and migration of hEM15A cells.

CONCLUSIONS

In conclusion, these results suggest that NYKF treatment promotes apoptosis and inhibits proliferation and migration in EMS by inhibiting the RAF/MEK/ERK signaling pathway by targeting RKIP.

Identification and Validation of a Novel Signature Based on NK Cell Marker Genes to Predict Prognosis and Immunotherapy Response in Lung Adenocarcinoma by Integrated Analysis of Single-Cell and Bulk RNA-Sequencing

Natural killer (NK) cells, the effectors of the innate immune system, have a remarkable influence on cancer prognosis and immunotherapy. In this study, a total of 1,816 samples from nine independent cohorts in public datasets were enrolled. We first conducted a comprehensive analysis of single-cell RNA-sequencing data of lung adenocarcinoma (LUAD) from the Gene Expression Omnibus (GEO) database and determined 189 NK cell marker genes. Subsequently, we developed a seven-gene prognostic signature based on NK cell marker genes in the TCGA LUAD cohort, which stratified patients into high-risk and low-risk groups. The predictive power of the signature was well verified in different clinical subgroups and GEO cohorts. With a multivariate analysis, the signature was identified as an independent prognostic factor. Low-risk patients had higher immune cell infiltration states, especially CD8⁺ T cells and follicular helper T cells. There existed a negative association between inflammatory activities and risk score, and the richness and diversity of the T-cell receptor (TCR) repertoire was higher in the low-risk groups. Importantly, analysis of an independent immunotherapy cohort (IMvigor210) revealed that low-risk patients had better immunotherapy responses and prognosis than high-risk patients. Collectively, our study developed a novel signature based on NK cell marker genes, which had a potent capability to predict the prognosis and immunotherapy response of LUAD patients.