Tissue differences revealed by gene expression profiles of various cell lines

Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL

Acute lymphoblastic leukemia (ALL) as a common cancer is a heterogeneous disease which is mainly divided into BCP-ALL and T-ALL, accounting for 80–85% and 15–20%, respectively. There are many differences between BCP-ALL and T-ALL, including prognosis, treatment, drug screening, gene research and so on. In this study, starting with methylation and gene expression data, we analyzed the molecular differences between BCP-ALL and T-ALL and identified the multi-omics signatures using Boruta and Monte Carlo feature selection methods. There were 7 expression signature genes (CD3D, VPREB3, HLA-DRA, PAX5, BLNK, GALNT6, SLC4A8) and 168 methylation sites corresponding to 175 methylation signature genes. The overall accuracy, accuracy of BCP-ALL, accuracy of T-ALL of the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) classifier using these signatures evaluated with 10-fold cross validation repeated 3 times were 0.973, 0.990, and 0.933, respectively. Two overlapped genes between 175 methylation signature genes and 7 expression signature genes were CD3D and VPREB3. The network analysis of the methylation and expression signature genes suggested that their common gene, CD3D, was not only different on both methylation and expression levels, but also played a key regulatory role as hub on the network. Our results provided insights of understanding the underlying molecular mechanisms of ALL and facilitated more precision diagnosis and treatment of ALL.

Identification and Analysis of the Blood lncRNA Signature for Liver Cirrhosis and Hepatocellular Carcinoma

As one of the most common malignant tumors, hepatocellular carcinoma (HCC) is the fifth major cause of cancer-associated mortality worldwide. In 90% of cases, HCC develops in the context of liver cirrhosis and chronic hepatitis B virus (HBV) infection is an important etiology for cirrhosis and HCC, accounting for 53% of all HCC cases. To understand the underlying mechanisms of the dynamic chain reactions from normal to HBV infection, from HBV infection to liver cirrhosis, from liver cirrhosis to HCC, we analyzed the blood lncRNA expression profiles from 38 healthy control samples, 45 chronic hepatitis B patients, 46 liver cirrhosis patients, and 46 HCC patients. Advanced machine-learning methods including Monte Carlo feature selection, incremental feature selection (IFS), and support vector machine (SVM) were applied to discover the signature associated with HCC progression and construct the prediction model. One hundred seventy-one key HCC progression-associated lncRNAs were identified and their overall accuracy was 0.823 as evaluated with leave-one-out cross validation (LOOCV). The accuracies of the lncRNA signature for healthy control, chronic hepatitis B, liver cirrhosis, and HCC were 0.895, 0.711, 0.870, and 0.826, respectively. The 171-lncRNA signature is not only useful for early detection and intervention of HCC, but also helpful for understanding the multistage tumorigenic processes of HCC.

The Methylation Pattern for Knee and Hip Osteoarthritis

Osteoarthritis is one of the most prevalent chronic joint diseases for middle-aged and elderly people. But in recent years, the number of young people suffering from the disease increases quickly. It is known that osteoarthritis is a common degenerative disease caused by the combination and interaction of many factors such as natural and environmental factors. DNA methylations reflect the effects of environmental factors. Several researches on DNA methylation at specific genes in OA cartilage indicated the great potential roles of DNA methylation in OA. To systematically investigate the methylation pattern in knee and hip osteoarthritis, we analyzed the methylation profiles in cartilage of 16 OA hip samples, 19 control hip samples and 62 OA knee samples. 12 discriminative methylation sites were identified using advanced minimal Redundancy Maximal Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The SVM classifier of these 12 methylation sites from genes like MEIS1, GABRG3, RXRA, and EN1, can perfectly classify the OA hip samples, control hip samples and OA knee samples evaluated with LOOCV (Leave-One Out-Cross Validation). These 12 methylation sites can not only serve as biomarker, but also provide underlying mechanism of OA.

Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies

Myocardial infarction (MI) is a type of serious heart attack in which the blood flow to the heart is suddenly interrupted, resulting in injury to the heart muscles due to a lack of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of MI, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of MI will help us choose a more reasonable treatment plan. Previously, comparative transcriptome studies focused on finding differentially expressed genes between MI patients and healthy people. However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. Subsequently, functional enrichment analyses followed by protein-protein interaction analysis on 134 genes identified several hub genes (IL1R1, TLR2, and TLR4) associated with progression of MI, which can be used as new diagnostic molecules for MI.

Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data

Single-cell sequencing technologies have emerged to address new and longstanding biological and biomedical questions. Previous studies focused on the analysis of bulk tissue samples composed of millions of cells. However, the genomes within the cells of an individual multicellular organism are not always the same. In this study, we aimed to identify the crucial and characteristically expressed genes that may play functional roles in tissue development and organogenesis, by analyzing a single-cell transcriptomic atlas of mice. We identified the most relevant gene features and decision rules classifying 18 cell categories, providing a list of genes that may perform important functions in the process of tissue development because of their tissue-specific expression patterns. These genes may serve as biomarkers to identify the origin of unknown cell subgroups so as to recognize specific cell stages/states during the dynamic process, and also be applied as potential therapy targets for developmental disorders.

Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms

Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in several aspects. Identifying their differentially expressed genes and different gene expression patterns can deepen our understanding of these two subtypes at the transcriptomic level. In this work, we used several machine learning algorithms to investigate the gene expression profiles of lung AC and lung SCC samples retrieved from Gene Expression Omnibus. First, the profiles were analyzed by using a powerful feature selection method, namely, Monte Carlo feature selection. A feature list, ranking all features according to their importance, and some informative features were obtained. Then, the feature list was used in the incremental feature selection method to extract optimal features, which can allow the support vector machine (SVM) to yield the best performance for classifying lung AC and lung SCC samples. Some top genes (CSTA, TP63, SERPINB13, CLCA2, BICD2, PERP, FAT2, BNC1, ATP11B, FAM83B, KRT5, PARD6G, PKP1) were extensively analyzed to prove that they can be differentially expressed genes between lung AC and lung SCC. Meanwhile, a rule learning procedure was applied on informative features to construct the classification rules. These rules provide a clear procedure of classification and show some different gene expression patterns between lung AC and lung SCC.

Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

Lung cancer is a highly prevalent type of cancer with a poor 5-year survival rate of about 4-17%. Eighty percent lung cancer belongs to non-small-cell lung cancer (NSCLC). For a long time, the treatment of NSCLC has been mostly guided by tumor stage, and there has been no significant difference between the therapy strategy of lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (SCLC), the two major subtypes of NSCLC. In recent years, important molecular differences between LUAD and SCLC are increasingly identified, indicating that targeted therapy will be more and more histologically specific in the future. To investigate the LUAD and SCLC difference on multi-omics scale, we analyzed the methylation and gene expression data together. With the Boruta method to remove irrelevant features and the MCFS (Monte Carlo Feature Selection) method to identify the significantly important features, we identified 113 key methylation features and 23 key gene expression features. HNF1B and TP63 were found to be dysfunctional on both methylation and gene expression levels. The experimentally determined interaction network suggested that TP63 may play an important role in connecting methylation genes and expression genes. Many of the discovered signature genes have been supported by literature. Our results may provide directions of precision diagnosis and therapy of LUAD and SCLC.

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

Complex diseases seriously affect people's physical and mental health. The discovery of disease-causing genes has become a target of research. With the emergence of bioinformatics and the rapid development of biotechnology, to overcome the inherent difficulties of the long experimental period and high cost of traditional biomedical methods, researchers have proposed many gene prioritization algorithms that use a large amount of biological data to mine pathogenic genes. However, because the currently known gene-disease association matrix is still very sparse and lacks evidence that genes and diseases are unrelated, there are limits to the predictive performance of gene prioritization algorithms. Based on the hypothesis that functionally related gene mutations may lead to similar disease phenotypes, this paper proposes a PU induction matrix completion algorithm based on heterogeneous information fusion (PUIMCHIF) to predict candidate genes involved in the pathogenicity of human diseases. On the one hand, PUIMCHIF uses different compact feature learning methods to extract features of genes and diseases from multiple data sources, making up for the lack of sparse data. On the other hand, based on the prior knowledge that most of the unknown gene-disease associations are unrelated, we use the PU-Learning strategy to treat the unknown unlabeled data as negative examples for biased learning. The experimental results of the PUIMCHIF algorithm regarding the three indexes of precision, recall, and mean percentile ranking (MPR) were significantly better than those of other algorithms. In the top 100 global prediction analysis of multiple genes and multiple diseases, the probability of recovering true gene associations using PUIMCHIF reached 50% and the MPR value was 10.94%. The PUIMCHIF algorithm has higher priority than those from other methods, such as IMC and CATAPULT.

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Lung cancer is a common malignant cancer. Kirsten rat sarcoma oncogene (KRAS) mutations have been considered as a key driver for lung cancers. KRAS p.G12C mutations were most predominant in NSCLC which was comprised about 11–16% of lung adenocarcinomas (p.G12C accounts for 45–50% of mutant KRAS). But it is still not clear how the KRAS mutation triggers lung cancers. To study the molecular mechanisms of KRAS mutation in lung cancer. We analyzed the gene expression profiles of 156 KRAS mutation samples and other negative samples with two stage feature selection approach: (1) minimal Redundancy Maximal Relevance (mRMR) and (2) Incremental Feature Selection (IFS). At last, 41 predictive genes for KRAS mutation were identified and a KRAS mutation predictor was constructed. Its leave one out cross validation MCC was 0.879. Our results were helpful for understanding the roles of KRAS mutation in lung cancer.

Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes

Copy number variation (CNV) is a common structural variation pattern of DNA, and it features a higher mutation rate than single-nucleotide polymorphisms (SNPs) and affects a larger fragment of genomes. CNV is related with the genesis of complex diseases and can thus be used as a strategy to identify novel cancer-predisposing markers or mechanisms. In particular, the frequent deletions of mono-ADP-ribosylhydrolase 2 (MACROD2) locus in human colorectal cancer (CRC) alters DNA repair and the sensitivity to DNA damage and results in chromosomal instability. The relationship between CNV and cancer has not been explained. In this study, on the basis of the genome variation profiling by the SNP array from 651 CRC primary tumors, we computationally analyzed the CNV data to select crucial SNP sites with the most relevance to three different states of MACROD2 (heterozygous deletion, homozygous deletion, and normal state), suggesting that these CNVs may play functional roles in CRC tumorigenesis. Our study can shed new insights into the genesis of cancer based on CNV, providing reference for clinical diagnosis, and treatment prognosis of CRC.

Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine

Gut microbiomes are integral microflora located in the human intestine with particular symbiosis. Among all microorganisms in the human intestine, bacteria are the most significant subgroup that contains many unique and functional species. The distribution patterns of bacteria in the human intestine not only reflect the different microenvironments in different sections of the intestine but also indicate that bacteria may have unique biological functions corresponding to their proper regions of the intestine. However, describing the functional differences between the bacterial subgroups and their distributions in different individuals is difficult using traditional computational approaches. Here, we first attempted to introduce four effective sets of bacterial features from independent databases. We then presented a novel computational approach to identify potential distinctive features among bacterial subgroups based on a systematic dataset on the gut microbiome from approximately 1,500 human gut bacterial strains. We also established a group of quantitative rules for explaining such distinctions. Results may reveal the microstructural characteristics of the intestinal flora and deepen our understanding on the regulatory role of bacterial subgroups in the human intestine.

Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas

Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by examining methylation data. Several advanced computational methods were used, such as Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support machine vector (SVM), etc. The MCFS method was adopted to analyze methylation features, resulting in a feature list. Then, the IFS method incorporating SVM was applied to the list to extract important methylation features and construct an optimal SVM classifier. As a result, several methylation features (sites) were found to relate to glioma subclasses, which are annotated onto multiple genes, such as FLJ37543, LCE3D, FAM89A, ADCY5, ESR1, C2orf67, REST, EPHA7, etc. These genes are enriched in biological functions, including cellular developmental process, neuron differentiation, cellular component morphogenesis, and G-protein-coupled receptor signaling pathway. Our results, which are supported by literature reports and independent dataset validation, showed that our identified genes and functions contributed to the detailed glioma subtypes. This study provided a basic research on IDH-mutation gliomas.

Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.

AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees

Mycobacterium tuberculosis is one of the most dangerous pathogens in humans. It acts as an etiological agent of tuberculosis (TB), infecting almost one-third of the world's population. Owing to the high incidence of multidrug-resistant TB and extensively drug-resistant TB, there is an urgent need for novel and effective alternative therapies. Peptide-based therapy has several advantages, such as diverse mechanisms of action, low immunogenicity, and selective affinity to bacterial cell envelopes. However, the identification of anti-tubercular peptides (AtbPs) via experimentation is laborious and expensive; hence, the development of an efficient computational method is necessary for the prediction of AtbPs prior to both in vitro and in vivo experiments. To this end, we developed a two-layer machine learning (ML)-based predictor called AtbPpred for the identification of AtbPs. In the first layer, we applied a two-step feature selection procedure and identified the optimal feature set individually for nine different feature encodings, whose corresponding models were developed using extremely randomized tree (ERT). In the second-layer, the predicted probability of AtbPs from the above nine models were considered as input features to ERT and developed the final predictor. AtbPpred respectively achieved average accuracies of 88.3% and 87.3% during cross-validation and an independent evaluation, which were ~8.7% and 10.0% higher than the state-of-the-art method. Furthermore, we established a user-friendly webserver which is currently available at http://thegleelab.org/AtbPpred. We anticipate that this predictor could be useful in the high-throughput prediction of AtbPs and also provide mechanistic insights into its functions.

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We developed a novel computational framework for the identification of anti-tubercular peptides using Extremely randomized tree.

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AtbPpred displayed superior performance compared to the existing method on both benchmark and independent datasets.

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We constructed a user-friendly web server that implements the proposed AtbPpred method.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

Evaluation of Plasma Extracellular Vesicle MicroRNA Signatures for Lung Adenocarcinoma and Granuloma With Monte-Carlo Feature Selection Method

Extracellular Vesicle (EV) is a compilation of secreted vesicles, including micro vesicles, large oncosomes, and exosomes. It can be used in non-invasive diagnosis. MicroRNAs (miRNAs) processed by exosomes can be detected by liquid biopsy. To objectively evaluate the discriminative ability of miRNAs from whole plasma, EV and EV-free plasma, we analyzed the miRNA expression profiles in whole plasma, EV and EV-free plasma of 10 lung adenocarcinoma and 9 granuloma patients. With Monte-Carlo feature selection method, the top discriminative miRNAs in whole plasma, EV and EV-free plasma were identified, and they were quite different. Using the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) method, we learned the classification rules: in whole plasma, granuloma patients did not express hsa-miR-223-3p while the lung adenocarcinoma patients expressed hsa-miR-223-3p; in EV, the hsa-miR-23b-3p was highly expressed in granuloma patients but not lung adenocarcinoma patients; in EV-free plasma, hsa-miR-376a-3p was expressed in granuloma patients but barely expressed in lung adenocarcinoma patients. For prediction performance, whole plasma had the highest weighted accuracy and EV outperformed EV-free plasma. Our results suggested that EV can be used as lung cancer biomarker. However, since it is less stable and not easy to detect, there are still technological difficulties to overcome.