DNA and protein methyltransferases inhibition by adenosine dialdehyde reduces the proliferation and migration of breast and lung cancer cells by downregulating autophagy

The Role of ER Stress and the Unfolded Protein Response in Cancer

Dysregulation of protein synthesis, folding, and secretion leads to endoplasmic reticulum (ER) stress, triggering the unfolded protein response (UPR). While the UPR is essential for cell survival under stress, its chronic activation in cancer cells supports tumorigenesis, metastasis, and chemoresistance by enabling cellular adaptation to hypoxia, nutrient deprivation, and oxidative stress. This review provides a comprehensive overview of the roles of key UPR mediators - binding immunoglobulin protein (BiP), protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), inositol-requiring enzyme 1α (IRE1α), and activating transcription factor 6 (ATF6) - in cancer progression and therapy resistance. Furthermore, it discusses strategies to target UPR pathways, including small molecule inhibitors, gene therapies, natural compounds, and combination therapies, while it evaluates their preclinical and clinical relevance. Finally, it explores how modulating UPR signaling can overcome therapeutic resistance, improve immunotherapy outcomes, and reshape the tumor microenvironment. This review emphasizes the promise of UPR-targeted approaches in enhancing the efficacy of current cancer treatments and achieving better patient outcomes.

PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs

Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, and small-molecule secondary metabolites. Dysregulation of methyltransferases is implicated in various human cancers. However, in light of the well-recognized significance of PMTs, reliable and efficient identification methods are essential. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated, and prediction models were trained using various machine-learning algorithms using a tenfold cross-validation technique. After evaluating each model on the dataset, the SVM-based CKSAAP model achieved the highest prediction accuracy with balanced sensitivity and specificity. Also, this SVM model outperformed deep-learning algorithms for the prediction of PMTs. In addition, cross-database validation was performed to ensure the robustness of the model. Feature importance was assessed using shapley additive explanations (SHAP) values, providing insights into the contributions of different features to the model's predictions. Finally, the SVM-based CKSAAP model was implemented in a standalone tool, PMTPred, due to its consistent performance during independent testing and cross-database evaluation. We believe that PMTPred will be a useful and efficient tool for the identification of PMTs. The PMTPred is freely available for download at https://github.com/ArvindYadav7/PMTPred and http://www.bioinfoindia.org/PMTPred/home.html for research and academic use.

Insights on E1-like enzyme ATG7: functional regulation and relationships with aging-related diseases

Autophagy is a dynamic self-renovation biological process that maintains cell homeostasis and is responsible for the quality control of proteins, organelles, and energy metabolism. The E1-like ubiquitin-activating enzyme autophagy-related gene 7 (ATG7) is a critical factor that initiates classic autophagy reactions by promoting the formation and extension of autophagosome membranes. Recent studies have identified the key functions of ATG7 in regulating the cell cycle, apoptosis, and metabolism associated with the occurrence and development of multiple diseases. This review summarizes how ATG7 is precisely programmed by genetic, transcriptional, and epigenetic modifications in cells and the relationship between ATG7 and aging-related diseases.

Global research and emerging trends in autophagy in lung cancer: a bibliometric and visualized study from 2013 to 2022

Purpose: To highlight the knowledge structure and evolutionary trends in research on autophagy in lung cancer. Methods: Research publications on autophagy in lung cancer were retrieved from the Web of Science Core Collection database. VOSviewer and CiteSpace data analysis software were used for the bibliometric and visualization analysis of countries, institutions, authors, journals, and keywords related to this field. Results: From 2013 to 2022, research on autophagy in lung cancer developed rapidly, showing rising trends in annual publications and citations. China (1,986 papers; 48,913 citations), Shandong University (77 publications; 1,460 citations), and Wei Zhang (20 publications; 342 citations) were the most productive and influential country, institution, and author, respectively. The journal with the most publications and citations on autophagy in lung cancer was the International Journal of Molecular Sciences (93 publications; 3,948 citations). An analysis of keyword co-occurrence showed that related research topics were divided into five clusters: 1) Mechanisms influencing autophagy in lung cancer and the role of autophagy in lung cancer; 2) Effect of autophagy on the biological behavior of lung cancer; 3) Regulatory mechanisms of 2 cell death processes: autophagy and apoptosis in lung cancer cells; 4) Role of autophagy in lung cancer treatment and drug resistance; and 5) Role of autophagy-related genes in the occurrence and development of lung cancer. Cell proliferation, migration, epithelial-mesenchymal transition, and tumor microenvironment were the latest high-frequency keywords that represented promising future research directions. Conclusion: This is the first comprehensive study describing the knowledge structure and emerging frontiers of research on autophagy in lung cancer from 2013 to 2022 by means of a bibliometric analysis. The study points to promising future research directions focusing on in-depth autophagy mechanisms, clinical applications, and potential therapeutic strategies, providing a valuable reference for researchers in the field. Systematic Review Registration: [https://systematicreview.gov/], identifier [registration number].