GABARAPL1 Inhibits EMT Signaling through SMAD-Tageted Negative Feedback

Revealing the impact of autophagy-related genes in rheumatoid arthritis: Insights from bioinformatics

Background

Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis.

Methods

In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results.

Results

The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated.

Conclusion

This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

A New Era of Integration between Multiomics and Spatio-Temporal Analysis for the Translation of EMT towards Clinical Applications in Cancer

Epithelial-mesenchymal transition (EMT) is crucial to metastasis by increasing cancer cell migration and invasion. At the cellular level, EMT-related morphological and functional changes are well established. At the molecular level, critical signaling pathways able to drive EMT have been described. Yet, the translation of EMT into efficient diagnostic methods and anti-metastatic therapies is still missing. This highlights a gap in our understanding of the precise mechanisms governing EMT. Here, we discuss evidence suggesting that overcoming this limitation requires the integration of multiple omics, a hitherto neglected strategy in the EMT field. More specifically, this work summarizes results that were independently obtained through epigenomics/transcriptomics while comprehensively reviewing the achievements of proteomics in cancer research. Additionally, we prospect gains to be obtained by applying spatio-temporal multiomics in the investigation of EMT-driven metastasis. Along with the development of more sensitive technologies, the integration of currently available omics, and a look at dynamic alterations that regulate EMT at the subcellular level will lead to a deeper understanding of this process. Further, considering the significance of EMT to cancer progression, this integrative strategy may enable the development of new and improved biomarkers and therapeutics capable of increasing the survival and quality of life of cancer patients.

TGF-β as Predictive Marker and Pharmacological Target in Lung Cancer Approach

Lung cancer (LC) represents the leading cause of cancer incidence and mortality worldwide. LC onset is strongly related to genetic mutations and environmental interactions, such as tobacco smoking, or pathological conditions, such as chronic inflammation. Despite advancement in knowledge of the molecular mechanisms involved in LC, this tumor is still characterized by an unfavorable prognosis, and the current therapeutic options are unsatisfactory. TGF-β is a cytokine that regulates different biological processes, particularly at the pulmonary level, and its alteration has been demonstrated to be associated with LC progression. Moreover, TGF-β is involved in promoting invasiveness and metastasis, via epithelial to mesenchymal transition (EMT) induction, where TGF-β is the major driver. Thus, a TGF-β-EMT signature may be considered a potential predictive marker in LC prognosis, and TGF-β-EMT inhibition has been demonstrated to prevent metastasis in various animal models. Concerning a LC therapeutic approach, some TGF-β and TGF-β-EMT inhibitors could be used in combination with chemo- and immunotherapy without major side effects, thereby improving cancer therapy. Overall, targeting TGF-β may be a valid possibility to fight LC, both in improving LC prognosis and cancer therapy, via a novel approach that could open up new effective strategies against this aggressive cancer.