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According to statistics, there are more than 19 million new cases and 10 million deaths every year.Early detection of cancer combined with existing treatments can significantly improve survival rates and treatment effects for various cancer types.Now, artificial intelligence (AI) promises to speed up this process, and doctors may soon be able to use AI to detect and diagnose cancer in patients, allowing for early treatment.Recently, a research team from Imperial College London and the University of Cambridge trained an artificial intelligence model, EMethylNET, to identify 13 different types of cancer (including breast cancer, liver cancer, lung cancer, prostate cancer, etc.) from non-cancerous tissues by observing DNA methylation patterns, with an accuracy of as high as 98.2%.The related paper is titled "Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns" and has been published in Biology Methods and Protocols.According to the paper, the model relies on tissue samples (rather than DNA fragments in blood) and is currently in the experimental stage, requiring additional training and testing on more diverse biopsy samples before it can be further used clinically.The researchers believe that an important significance of this study is the use of an explainable artificial intelligence model to provide explanations of the logic behind its predictions.The study also explored the inner workings of their model and found significant improvements in understanding the processes underlying carcinogenesis.The multi-classification model performed well, with an accuracy of over 98%Cancer has always been one of the most challenging diseases facing humanity.The evolving nature of cancer is extremely complex, and treatment becomes more difficult over time as it is discovered.Early screening of cancer is crucial and is one of the important directions that the medical community has been working hard to overcome.Genetic information is encoded in the pattern of four bases (A, T, G, and C) in DNA.Changes in the environment outside the cell can cause certain DNA bases to be modified by adding methyl groups, a process called "DNA methylation."Every cell has millions of these DNA methylation marks.Researchers observed changes in these markers during the early development of cancer and concluded that they may help in early diagnosis of cancer.Identifying DNA methylation signatures specific to different cancer types can be as difficult as finding a needle in a haystack.In this work, the research team used machine learning methods to identify cancer-specific changes from normal tissue-specific methylation, leveraging DNA methylation microarray data from 13 cancer types and corresponding normal tissues.Methylome data based on Illumina Infinium arrays, and data were extracted, cleaned, and processed as described in Methods.This methylation microarray data was analyzed using a pair of methylated and unmethylated probes to determine the ratio of the methylated probe intensity to the overall intensity for a given CpG position (called the beta value).They trained and evaluated four different model types: logistic regression, support vector machine (SVM), gradient boosted decision tree (XGBoost), and deep neural network (DNN).For the first three model types, binary and multi-class models were created.Since the binary logistic regression model does not perform significantly better than the binary XGBoost model, and the MCC score of multi-class logistic regression is lower than multi-class XGBoost and DNN, the study focuses the analysis on XGBoost and DNN.Most binary XGBoost models (trained on TCGA data) performed well when tested on these independent datasets.To create a more robust model and improve these results, the researchers designed EMethylNET, a model consisting of a DNN model trained on features learned from multi-class XGBoost to further improve performance.Figure | Method OverviewDetecting cancer status by binary classification of DNA methylation in individual tumors and normal tissues, 5 out of 13 models (COAD, KIRC, LUAD, LUSC, and UCEC) achieved perfect test set performance.Across all models, the average accuracy is 98.7% and the average MCC (a performance metric that is not affected by severe class imbalance) is 91.9%.They trained a multi-class XGBoost model on the entire training data, which could distinguish 13 cancer types from normal samples with high accuracy, with an overall accuracy of 98.2% and an overall MCC of 98.0%.At the same time, the model achieves high accuracy on independent heterogeneous data sets and also shows good performance on independent data sets.Figure | Performance of binary XGBoost models on independent datasetsThe literature on the use of methylation-based methods for cancer detection and classification is large and growing.A comparative analysis of EMethylNET with other related studies is conducted, demonstrating that EMethylNET achieves competitive test set performance among similar works.Table | Summary of related researchMultiple classes of genes are closely related to cancer-related processesA key advantage of using interpretable methods such as XGBoost is that features for classification can be identified. The research team explored PCC from the multi-class XGBoost model (i.e., the input features of EMethylNET).PCCs can be mapped to proximal genes鈥攇enes whose gene bodies or promoter regions (as a 1500 base pair window upstream of the transcription start site) overlap with PCCs. Genes obtained by mapping multiclass PCCs to proximal genes are called 鈥渕ulticlass genes.鈥?/p>They performed functional enrichment analysis on multiple types of genes and found that they are enriched in genes that contribute to carcinogenesis and transcriptional regulatory characteristics, and are enriched in cancer-related pathways and networks.The multigenome consists of 229 known tumor suppressors and oncogenes, 546 transcriptional regulators, and is involved in a wide range of cancer-related pathways and processes.In addition, they found that the gene list contained many non-coding RNA genes, mainly composed of lncRNAs.This is consistent with a growing body of research suggesting that lncRNAs and other non-coding RNAs play a critical role in carcinogenesis.Compared with related studies, this study is the first to provide in-depth feature analysis where CpGs are freely selected by the model without prior feature selection that would add potential bias to the feature analysis results.Is AI predicting cancer just around the corner? "With better training on more diverse data and rigorous testing in the clinic, computational methods like this will eventually provide AI models that can help doctors with early detection and screening of cancer," said Samith A Samarajiwa, the paper's corresponding author."This will provide better treatment outcomes."Depending on the availability of training data, this method could be expanded to detect hundreds of cancer types.Future applications include extending this approach to DNA methylation data on cell-free DNA, with the ultimate goal of early detection of many types of cancer through liquid biopsy methods.In addition, a clear clinical application of this method is to screen for specific cancer types or cancers of unknown origin, and although the current model is not optimized for this purpose, there is room for expansion in this area.Reference link:https://academic.oup.com/biomethods/article/9/1/bpae028/7696058