Autophagy and Tumor Database: ATdb, a novel database connecting autophagy and tumor

Modulating autophagy to treat diseases: A revisited review on in silico methods

BACKGROUND

Autophagy refers to the conserved cellular catabolic process relevant to lysosome activity and plays a vital role in maintaining the dynamic equilibrium of intracellular matter by degrading harmful and abnormally accumulated cellular components. Accumulating evidence has recently revealed that dysregulation of autophagy by genetic and exogenous interventions may disrupt cellular homeostasis in human diseases. In silico approaches as powerful aids to experiments have also been extensively reported to play their critical roles in the storage, prediction, and analysis of massive amounts of experimental data. Thus, modulating autophagy to treat diseases by in silico methods would be anticipated.

AIM OF REVIEW

Here, we focus on summarizing the updated in silico approaches including databases, systems biology network approaches, omics-based analyses, mathematical models, and artificial intelligence (AI) methods that sought to modulate autophagy for potential therapeutic purposes, which will provide a new insight into more promising therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Autophagy-related databases are the data basis of the in silico method, storing a large amount of information about DNA, RNA, proteins, small molecules and diseases. The systems biology approach is a method to systematically study the interrelationships among biological processes including autophagy from a macroscopic perspective. Omics-based analyses are based on high-throughput data to analyze gene expression at different levels of biological processes involving autophagy. mathematical models are visualization methods to describe the dynamic process of autophagy, and its accuracy is related to the selection of parameters. AI methods use big data related to autophagy to predict autophagy targets, design targeted small molecules, and classify diverse human diseases for potential therapeutic applications.

PINK1-PTEN axis promotes metastasis and chemoresistance in ovarian cancer via non-canonical pathway

BACKGROUND

Ovarian cancer is commonly associated with a poor prognosis due to metastasis and chemoresistance. PINK1 (PTEN-induced kinase 1) is a serine/threonine kinase that plays a crucial part in regulating various physiological and pathophysiological processes in cancer cells.

METHODS

The ATdb database and "CuratedOvarianData" were used to evaluate the effect of kinases on ovarian cancer survival. The gene expression in ovarian cancer cells was detected by Western blot and quantitative real-time PCR. The effects of gene knockdown or overexpression in vitro were evaluated by wound healing assay, cell transwell assay, immunofluorescence staining, immunohistochemistry, and flow cytometry analysis. Mass spectrometry analysis, protein structure analysis, co-immunoprecipitation assay, nuclear-cytoplasmic separation, and in vitro kinase assay were applied to demonstrate the PINK1-PTEN (phosphatase and tensin homolog) interaction and the effect of this interaction. The metastasis experiments for ovarian cancer xenografts were performed in female BALB/c nude mice.

RESULTS

PINK1 was strongly associated with a poor prognosis in ovarian cancer patients and promoted metastasis and chemoresistance in ovarian cancer cells. Although the canonical PINK1/PRKN (parkin RBR E3 ubiquitin protein ligase) pathway showed weak effects in ovarian cancer, PINK1 was identified to interact with PTEN and phosphorylate it at Serine179. Remarkably, the phosphorylation of PTEN resulted in the inactivation of the phosphatase activity, leading to an increase in AKT (AKT serine/threonine kinase) activity. Moreover, PINK1-mediated phosphorylation of PTEN impaired the nuclear import of PTEN, thereby enhancing the cancer cells' ability to resist chemotherapy and metastasize.

CONCLUSIONS

PINK1 interacts with and phosphorylates PTEN at Serine179, resulting in the activation of AKT and the inhibition of PTEN nuclear import. PINK1 promotes ovarian cancer metastasis and chemotherapy resistance through the regulation of PTEN. These findings offer new potential therapeutic targets for ovarian cancer management.

Canonical and Noncanonical ER Stress-Mediated Autophagy Is a Bite the Bullet in View of Cancer Therapy

Cancer cells adapt multiple mechanisms to counter intense stress on their way to growth. Tumor microenvironment stress leads to canonical and noncanonical endoplasmic stress (ER) responses, which mediate autophagy and are engaged during proteotoxic challenges to clear unfolded or misfolded proteins and damaged organelles to mitigate stress. In these conditions, autophagy functions as a cytoprotective mechanism in which malignant tumor cells reuse degraded materials to generate energy under adverse growing conditions. However, cellular protection by autophagy is thought to be complicated, contentious, and context-dependent; the stress response to autophagy is suggested to support tumorigenesis and drug resistance, which must be adequately addressed. This review describes significant findings that suggest accelerated autophagy in cancer, a novel obstacle for anticancer therapy, and discusses the UPR components that have been suggested to be untreatable. Thus, addressing the UPR or noncanonical ER stress components is the most effective approach to suppressing cytoprotective autophagy for better and more effective cancer treatment.

AMTDB: A comprehensive database of autophagic modulators for anti-tumor drug discovery

Autophagy, originally described as a mechanism for intracellular waste disposal and recovery, has been becoming a crucial biological process closely related to many types of human tumors, including breast cancer, osteosarcoma, glioma, etc., suggesting that intervention of autophagy is a promising therapeutic strategy for cancer drug development. Therefore, a high-quality database is crucial for unraveling the complicated relationship between autophagy and human cancers, elucidating the crosstalk between the key autophagic pathways, and autophagic modulators with their remarkable antitumor activities. To achieve this goal, a comprehensive database of autophagic modulators (AMTDB) was developed. AMTDB focuses on 153 cancer types, 1,153 autophagic regulators, 860 targets, and 2,046 mechanisms/signaling pathways. In addition, a variety of classification methods, advanced retrieval, and target prediction functions are provided exclusively to cater to the different demands of users. Collectively, AMTDB is expected to serve as a powerful online resource to provide a new clue for the discovery of more candidate cancer drugs.

iPCD: A Comprehensive Data Resource of Regulatory Proteins in Programmed Cell Death

Programmed cell death (PCD) is an essential biological process involved in many human pathologies. According to the continuous discovery of new PCD forms, a large number of proteins have been found to regulate PCD. Notably, post-translational modifications play critical roles in PCD process and the rapid advances in proteomics have facilitated the discovery of new PCD proteins. However, an integrative resource has yet to be established for maintaining these regulatory proteins. Here, we briefly summarize the mainstream PCD forms, as well as the current progress in the development of public databases to collect, curate and annotate PCD proteins. Further, we developed a comprehensive database, with integrated annotations for programmed cell death (iPCD), which contained 1,091,014 regulatory proteins involved in 30 PCD forms across 562 eukaryotic species. From the scientific literature, we manually collected 6493 experimentally identified PCD proteins, and an orthologous search was then conducted to computationally identify more potential PCD proteins. Additionally, we provided an in-depth annotation of PCD proteins in eight model organisms, by integrating the knowledge from 102 additional resources that covered 16 aspects, including post-translational modification, protein expression/proteomics, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein–protein interaction, drug–target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, subcellular localization and DNA and RNA element. With a data volume of 125 GB, we anticipate that iPCD can serve as a highly useful resource for further analysis of PCD in eukaryotes.

A Novel Risk Model Based on Autophagy-Related LncRNAs Predicts Prognosis and Indicates Immune Infiltration Landscape of Patients With Cutaneous Melanoma

Cutaneous melanoma (CM) is a malignant tumor with a high incidence rate and poor prognosis. Autophagy plays an essential role in the development of CM; however, the role of autophagy-related long noncoding RNAs (lncRNAs) in this process remains unknown. Human autophagy-related genes were extracted from the Human Autophagy Gene Database and screened for autophagy-related lncRNAs using Pearson correlation. Multivariate Cox regression analysis was implemented to identify ten autophagy-related lncRNAs associated with prognosis, and a risk model was constructed. The Kaplan-Meier survival curve showed that the survival probability of the high-risk group was lower than that of the low-risk group. A novel predictive model was constructed to investigate the independent prognostic value of the risk model. The nomogram results showed that the risk score was an independent prognostic signature that distinguished it from other clinical characteristics. The immune infiltration landscape of the low-risk and high-risk groups was further investigated. The low-risk groups displayed higher immune, stromal, and ESTIMATE scores and lower tumor purity. The CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms indicated a notable gap in immune cells between the low- and high-risk groups. Ten autophagy-related lncRNAs were significantly correlated with immune cells. Finally, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) results demonstrated that autophagy-related lncRNA-mediated and immune-related signaling pathways are crucial factors in regulating CM. Altogether, these data suggest that constructing a risk model based on ten autophagy-related lncRNAs can accurately predict prognosis and indicate the tumor microenvironment of patients with CM. Thus, our study provides a new perspective for the future clinical treatment of CM.

Characterization of an Autophagy-Immune Related Genes Score Signature and Prognostic Model and its Correlation with Immune Response for Bladder Cancer

Purpose

The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases.

Methods

The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model.

Results

Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy.

Conclusion

We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.

An exploratory text analysis of the autophagy research field

After its discovery in the 1950 s, the autophagy research field has seen its annual number of publications climb from tens to thousands. The ever-growing number of autophagy publications is a wealth of information but presents a challenge to researchers, especially those new to the field, who are looking for a general overview of the field to, for example, determine current topics of the field or formulate new hypotheses. Here, we employed text mining tools to extract research trends in the autophagy field, including those of genes, terms, and topics. The publication trend of the field can be separated into three phases. The exponential rise in publication number began in the last phase and is most likely spurred by a series of highly cited research papers published in previous phases. The exponential increase in papers has resulted in a larger variety of research topics, with the majority involving those that are directly physiologically relevant, such as disease and modulating autophagy. Our findings provide researchers a summary of the history of the autophagy research field and perhaps hints of what is to come.Abbreviations: 5Y-IF: 5-year impact factor; AIS: article influence score; EM: electron microscopy; HGNC: HUGO gene nomenclature committee; LDA: latent Dirichlet allocation; MeSH: medical subject headings; ncRNA: non-coding RNA.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.