ArticleScientific reports2020
Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Multiphase CT-derived markers for the characterization of large solid and cystic renal masses by histology and grade.Polish journal of radiology · 2026Article
- Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma.Journal of clinical medicine · 2025Review
- Autophagy as a potential therapeutic target in regulating improper cellular proliferation.Frontiers in pharmacology · 2025Review
- Explainable AI to unveil cellular autophagy dynamics.PloS one · 2025Article
- Autophagy and machine learning: Unanswered questions.Biochimica et biophysica acta. Molecular basis of disease · 2024Review
- Modulating autophagy to treat diseases: A revisited review on in silico methods.Journal of advanced research · 2024Review
- The Molecular Mechanism and Therapeutic Application of Autophagy for Urological Disease.International journal of molecular sciences · 2023Review
- Systems biology of autophagy in leishmanial infection and its diverse role in precision medicine.Frontiers in molecular biosciences · 2023Review
- Autophagy and beyond: Unraveling the complexity of UNC-51-like kinase 1 (ULK1) from biological functions to therapeutic implications.Acta pharmaceutica Sinica. B · 2022Review
- Artificial intelligence for renal cancer: From imaging to histology and beyond.Asian journal of urology · 2022Review
- A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning.Frontiers in endocrinology · 2022Article
- An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Tracing the footsteps of autophagy in computational biology.Briefings in bioinformatics · 2021Article
- Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors.BMC bioinformatics · 2020Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning techniques have been previously applied for classification of tumors based largely on morphological features of tumor cells recognized in H&E images. Here, we tested the possibility of using numeric data acquired from software-based quantification of certain marker proteins, i.e. key autophagy proteins (ATGs), obtained from immunohistochemical (IHC) images of renal cell carcinomas (RCC). Using IHC staining and automated image quantification with a tissue microarray (TMA) of RCC, we found ATG1, ATG5 and microtubule-associated proteins 1A/1B light chain 3B (LC3B) were significantly reduced, suggesting a reduction in the basal level of autophagy with RCC. Notably, the levels of the ATG proteins expressed did not correspond to the mRNA levels expressed in these tissues. Applying a supervised machine learning algorithm, the K-Nearest Neighbor (KNN), to our quantified numeric data revealed that LC3B provided a strong measure for discriminating clear cell RCC (ccRCC). ATG5 and sequestosome-1 (SQSTM1/p62) could be used for classification of chromophobe RCC (crRCC). The quantitation of particular combinations of ATG1, ATG16L1, ATG5, LC3B and p62, all of which measure the basal level of autophagy, were able to discriminate among normal tissue, crRCC and ccRCC, suggesting that the basal level of autophagy would be a potentially useful parameter for RCC discrimination. In addition to our observation that the basal level of autophagy is reduced in RCC, our workflow from quantitative IHC analysis to machine learning could be considered as a potential complementary tool for the classification of RCC subtypes and also for other types of tumors for which precision medicine requires a characterization.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.