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Classifying lung cancer subtypes, which are characterized by multi-microRNAs (miRNAs) upregulation, is important for therapy and prognosis evaluation. Liquid biopsy is a promising approach, but the pretreatment of RNA extraction is labor-intensive and impairs accuracy. Here we develop size-coded hydrogel microbeads for extraction-free quantification of miR-21, miR-205, and miR-375 directly from serum. The hydrogel microbead is immobilized with an miRNA capture probe, which well retains target miRNA and provides good nonfouling capability for nonspecific biomolecules in serum. The porous structure of microbeads allows efficient DNA cascade amplification reaction and generates a fluorescence signal. The microbeads are clustered into three groups according to size via flow cytometry sorting, and the group fluorescence is integrated for the corresponding miRNA quantification. With machine-learning-assisted data analysis, it achieves good lung cancer diagnosis accuracy and 80% accuracy for subtype classification for 108 serum samples, including lung cancer patients and healthy controls.

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

Cancer and its subtypes constitute approximately 30% of all causes of death globally and display a wide range of heterogeneity in terms of clinical and molecular responses to therapy. Molecular subtyping has enabled the use of precision medicine to overcome these challenges and provide significant biological insights to predict prognosis and improve clinical decision-making. Over the past decade, conventional machine learning (ML) and deep learning (DL) algorithms have been widely espoused for the classification of cancer subtypes from gene expression datasets. However, these methods are potentially biased toward the identification of cancer biomarkers. Hence, an end-to-end deep learning approach, DeepGene Transformer, is proposed which addresses the complexity of high-dimensional gene expression with a multi-head self-attention module by identifying relevant biomarkers across multiple cancer subtypes without requiring feature selection as a pre-requisite for the current classification algorithms. Comparative analysis reveals that the proposed DeepGene Transformer outperformed the commonly used traditional and state-of-the-art classification algorithms and can be considered an efficient approach for classifying cancer and its subtypes, indicating that any improvement in deep learning models in computational biologists can be reflected well in this domain as well. • A Transformer-based framework for accurate classification of cancer and its subtypes. • Representations learned from attention mechanism significantly increase accuracies. • DeepGene Transformer unveils distinct patterns in gene expressions and makes predictions. • Effectiveness of multi-head self-attention layer as a convolution layer is evaluated. • Proposed DeepGene Transformer is superior to current algorithms for automatic diagnosis. [ABSTRACT FROM AUTHOR]