Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes

Autophagy and machine learning: Unanswered questions

Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.

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.

The Molecular Mechanism and Therapeutic Application of Autophagy for Urological Disease

Autophagy is a lysosomal degradation process known as autophagic flux, involving the engulfment of damaged proteins and organelles by double-membrane autophagosomes. It comprises microautophagy, chaperone-mediated autophagy (CMA), and macroautophagy. Macroautophagy consists of three stages: induction, autophagosome formation, and autolysosome formation. Atg8-family proteins are valuable for tracking autophagic structures and have been widely utilized for monitoring autophagy. The conversion of LC3 to its lipidated form, LC3-II, served as an indicator of autophagy. Autophagy is implicated in human pathophysiology, such as neurodegeneration, cancer, and immune disorders. Moreover, autophagy impacts urological diseases, such as interstitial cystitis /bladder pain syndrome (IC/BPS), ketamine-induced ulcerative cystitis (KIC), chemotherapy-induced cystitis (CIC), radiation cystitis (RC), erectile dysfunction (ED), bladder outlet obstruction (BOO), prostate cancer, bladder cancer, renal cancer, testicular cancer, and penile cancer. Autophagy plays a dual role in the management of urologic diseases, and the identification of potential biomarkers associated with autophagy is a crucial step towards a deeper understanding of its role in these diseases. Methods for monitoring autophagy include TEM, Western blot, immunofluorescence, flow cytometry, and genetic tools. Autophagosome and autolysosome structures are discerned via TEM. Western blot, immunofluorescence, northern blot, and RT-PCR assess protein/mRNA levels. Luciferase assay tracks flux; GFP-LC3 transgenic mice aid study. Knockdown methods (miRNA and RNAi) offer insights. This article extensively examines autophagy's molecular mechanism, pharmacological regulation, and therapeutic application involvement in urological diseases.

Xihuang Pill-destabilized CD133/EGFR/Akt/mTOR cascade reduces stemness enrichment of glioblastoma via the down-regulation of SOX2

BACKGROUND

Our previous study found that XHP could induce GBM cells to undergo apoptosis. A lot of evidence suggests that glioma stem-like cells (GSCs) are key factors that contribute to disease progression and poor prognosis of glioblastoma multiforme (GBM). Traditional Chinese medicine has been applied in clinical practice as a complementary and alternative therapy for glioma.

PURPOSE

To evaluate the effect and the potential molecular mechanism of Xihuang pill (XHP) on GSCs.

METHODS

UPLC-QTOF-MS analysis was used for constituent analysis of XHP. Using network pharmacology and bioinformatics methods, a molecular network targeting GSCs by the active ingredients in XHP was constructed. Cell viability, self-renewal ability, apoptosis, and GSC markers were detected by CCK-8 assay, tumor sphere formation assay and flow cytometry, respectively. The interrelationship between GSC markers (CD133 and SOX2) and key proteins of the EGFR/Akt/mTOR signaling pathway was evaluated using GEPIA and verified by western blot. A GBM cell line stably overexpressing Akt was constructed using lentivirus to evaluate the role of Akt signaling in the regulation of glioma stemness. The effect of XHP on glioma growth was analyzed by a subcutaneously transplanted glioma cell model in nude mice, hematoxylin-eosin staining was used to examine pathological changes, TUNEL staining was used to detect apoptosis in tumor tissues, and the expression of GSC markers in tumor tissues was identified by western blot and immunofluorescence.

RESULTS

Bioinformatics analysis showed that 55 matched targets were related to XHP targets and glioma stem cell targets. In addition to causing apoptosis, XHP could diminish the number of GBM 3D spheroids, the proportion of CD133-positive cells and the expression level of GSC markers (CD133 and SOX2) in vitro. Furthermore, XHP could attenuate the expression of CD133, EGFR, p-Akt, p-mTOR and SOX2 in GBM spheres. Overexpression of Akt significantly increased the expression level of SOX2, which was prohibited in the presence of XHP. XHP reduced GSC markers including CD133 and SOX2, and impeded the development of glioma growth in xenograft mouse models in vivo.

CONCLUSION

We demonstrate for the first time that XHP down-regulates stemness, restrains self-renewal and induces apoptosis in GSCs and impedes glioma growth by down-regulating SOX2 through destabilizing the CD133/EGFR/Akt/mTOR cascade.

A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning

Background

Renal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment.

Methods

The retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model.

Results

Bone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM.

Conclusions

This XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.

Systems biology of autophagy in leishmanial infection and its diverse role in precision medicine

Autophagy is a contentious issue in leishmaniasis and is emerging as a promising therapeutic regimen. Published research on the impact of autophagic regulation on Leishmania survival is inconclusive, despite numerous pieces of evidence that Leishmania spp. triggers autophagy in a variety of cell types. The mechanistic approach is poorly understood in the Leishmania parasite as autophagy is significant in both Leishmania and the host. Herein, this review discusses the autophagy proteins that are being investigated as potential therapeutic targets, the connection between autophagy and lipid metabolism, and microRNAs that regulate autophagy and lipid metabolism. It also highlights the use of systems biology to develop novel autophagy-dependent therapeutics for leishmaniasis by utilizing artificial intelligence (AI), machine learning (ML), mathematical modeling, network analysis, and other computational methods. Additionally, we have shown many databases for autophagy and metabolism in Leishmania parasites that suggest potential therapeutic targets for intricate signaling in the autophagy system. In a nutshell, the detailed understanding of the dynamics of autophagy in conjunction with lipids and miRNAs unfolds larger dimensions for future research.

An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence

In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors' time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.

Artificial intelligence for renal cancer: From imaging to histology and beyond

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.

Autophagy and beyond: Unraveling the complexity of UNC-51-like kinase 1 (ULK1) from biological functions to therapeutic implications

UNC-51-like kinase 1 (ULK1), as a serine/threonine kinase, is an autophagic initiator in mammals and a homologous protein of autophagy related protein (Atg) 1 in yeast and of UNC-51 in Caenorhabditis elegans. ULK1 is well-known for autophagy activation, which is evolutionarily conserved in protein transport and indispensable to maintain cell homeostasis. As the direct target of energy and nutrition-sensing kinase, ULK1 may contribute to the distribution and utilization of cellular resources in response to metabolism and is closely associated with multiple pathophysiological processes. Moreover, ULK1 has been widely reported to play a crucial role in human diseases, including cancer, neurodegenerative diseases, cardiovascular disease, and infections, and subsequently targeted small-molecule inhibitors or activators are also demonstrated. Interestingly, the non-autophagy function of ULK1 has been emerging, indicating that non-autophagy-relevant ULK1 signaling network is also linked with diseases under some specific contexts. Therefore, in this review, we summarized the structure and functions of ULK1 as an autophagic initiator, with a focus on some new approaches, and further elucidated the key roles of ULK1 in autophagy and non-autophagy. Additionally, we also discussed the relationships between ULK1 and human diseases, as well as illustrated a rapid progress for better understanding of the discovery of more candidate small-molecule drugs targeting ULK1, which will provide a clue on novel ULK1-targeted therapeutics in the future.

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

Kidney cancer management 3.0: can artificial intelligence make us better?

PURPOSE OF REVIEW

Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space.

RECENT FINDINGS

Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses.

SUMMARY

Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.

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.

Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors

Background

Classification of certain proteins with specific functions is momentous for biological research. Encoding approaches of protein sequences for feature extraction play an important role in protein classification. Many computational methods (namely classifiers) are used for classification on protein sequences according to various encoding approaches. Commonly, protein sequences keep certain labels corresponding to different categories of biological functions (e.g., bacterial type IV secreted effectors or not), which makes protein prediction a fantasy. As to protein prediction, a kernel set of protein sequences keeping certain labels certified by biological experiments should be existent in advance. However, it has been hardly ever seen in prevailing researches. Therefore, unsupervised learning rather than supervised learning (e.g. classification) should be considered. As to protein classification, various classifiers may help to evaluate the effectiveness of different encoding approaches. Besides, variable selection from an encoded feature representing protein sequences is an important issue that also needs to be considered.

Results

Focusing on the latter problem, we propose a new method for variable selection from an encoded feature representing protein sequences. Taking a benchmark dataset containing 1947 protein sequences as a case, experiments are made to identify bacterial type IV secreted effectors (T4SE) from protein sequences, which are composed of 399 T4SE and 1548 non-T4SE. Comparable and quantified results are obtained only using certain components of the encoded feature, i.e., position-specific scoring matix, and that indicates the effectiveness of our method.

Conclusions

Certain variables other than an encoded feature they belong to do work for discrimination between different types of proteins. In addition, ensemble classifiers with an automatic assignment of different base classifiers do achieve a better classification result.