Alternative Polyadenylation Modification Patterns Reveal Essential Posttranscription Regulatory Mechanisms of Tumorigenesis in Multiple Tumor Types

Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data

Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein-protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.

Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

Identification of Protein Subcellular Localization With Network and Functional Embeddings

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.