• Humans
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/therapy
• Carcinoma, Hepatocellular/pathology
• Liver Neoplasms/genetics
• Liver Neoplasms/therapy
• Liver Neoplasms/pathology
• Epigenesis, Genetic
• Biomarkers, Tumor/genetics
• DNA Methylation
• Gene Expression Regulation, Neoplastic
• MicroRNAs/genetics

• HBV
• HCC
• bioinformatics analysis
• genomic signatures
• machine learning approach

• Biomarker
• Diethyl nitrosamine
• Hepatocellular carcinoma
• Next genome sequencing (NGS)
• miRNAs

• MT: Regular Issue
• cancer survival estimation
• kidney cancer
• machine learning
• miRNA signature
• telomere maintenance genes

• Glioma; grade; circulating miRNA; blood plasma; NanoString; biomarker

• Humans
• Glioma/blood
• Glioma/genetics
• Glioma/pathology
• Brain Neoplasms/blood
• Brain Neoplasms/genetics
• Brain Neoplasms/pathology
• Circulating MicroRNA/blood
• Circulating MicroRNA/genetics
• Male
• Female
• Cross-Sectional Studies
• Middle Aged
• Biomarkers, Tumor/blood
• Biomarkers, Tumor/genetics
• Adult
• Neoplasm Grading
• MicroRNAs/blood
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic

• Aurora kinase A
• circular RNA
• hepatocellular carcinoma
• long-non-coding RNA
• microRNA

• artificial intelligence
• cancer screening and classification
• drug discovery
• multi-omics
• prognosis prediction

• Humans
• Neoplasms/genetics
• Artificial Intelligence
• Genomics

• Humans
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Colonic Neoplasms/genetics
• Colonic Neoplasms/diagnosis
• Colonic Neoplasms/pathology
• Biomarkers, Tumor/genetics
• Adenocarcinoma/genetics
• Adenocarcinoma/diagnosis
• Adenocarcinoma/pathology
• Neoplasm Staging
• Gene Expression Profiling/methods
• Machine Learning
• Algorithms

• Humans
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/pathology
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Liver Neoplasms/genetics
• Liver Neoplasms/pathology
• Prognosis
• Gene Expression Profiling/methods
• Gene Expression Regulation, Neoplastic

• R01 GM130715 NIGMS NIH HHS
• Marshfield Clinic Research Institute
• MCRI Weber Endowment

• HCC diagnosis
• HCC prognosis
• angiogenesis
• autophagy
• biomarker
• hepatocellular carcinoma
• miRNA

• Humans
• Carcinoma, Hepatocellular/diagnosis
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/metabolism
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Liver Neoplasms/diagnosis
• Liver Neoplasms/genetics
• Liver Neoplasms/metabolism
• Quality of Life
• Cell Line, Tumor
• Prognosis
• Gene Expression Regulation, Neoplastic

• Apostichopus japonicus
• Cutaneous wound healing
• Gelsolin
• miRn-3

• Animals
• Stichopus/genetics
• Stichopus/metabolism
• Sea Cucumbers/genetics
• Sea Cucumbers/metabolism
• Gelsolin/metabolism
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Wound Healing/genetics
• Immunity, Innate

• Humans
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/therapy
• Carcinoma, Hepatocellular/metabolism
• Enhancer of Zeste Homolog 2 Protein/genetics
• Enhancer of Zeste Homolog 2 Protein/metabolism
• Enhancer of Zeste Homolog 2 Protein/pharmacology
• Liver Neoplasms/genetics
• Liver Neoplasms/metabolism
• Liver Neoplasms/pathology
• MicroRNAs/genetics
• Cell Proliferation/genetics
• Cell Movement/genetics
• Gene Expression Regulation, Neoplastic

• Humans
• Carcinoma, Hepatocellular/pathology
• Prognosis
• Liver Neoplasms/pathology
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Hepatitis B

• Marshfield Clinic Research Institute

• liver cancer
• non-coding RNAs
• translational medicine

• Let-7i
• cancer
• dual mechanism
• inhibitor
• promoter

• hepatocellular carcinoma
• microRNA–gene interaction network
• network biomarker

• 61903107 National Natural Science Foundation of China

• hepatocellular carcinoma
• genetic classification
• molecular classification
• immune phenotype

• Humans
• Carcinoma, Hepatocellular/diagnosis
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/therapy
• Liver Neoplasms/diagnosis
• Liver Neoplasms/genetics
• Liver Neoplasms/therapy
• Genomics

• R01 AA023146 NIAAA NIH HHS
• R01 CA236591 NCI NIH HHS
• U01 CA230690 NCI NIH HHS

• Artificial Intelligence
• Cancer diagnosis prediction
• Machine learning
• cancer early stage detection
• pan-cancer analysis

• Cancer survival
• Head and neck cancer
• Machine learning
• miRNA signature

• Hsa-miR-214-3p
• biomarkers
• hsa-miR-3178
• hsa-miR-3182
• hsa-miR-4488
• next generation sequencing

• cancer genomics
• machine learning

• Chemopreventive
• Hepatocellular carcinoma
• Liver cancer
• Natural products
• Signaling pathways
• Therapeutic targets

• mirnas
• dna damage response

• alpha-fetoprotein
• biomarker
• circulating nucleic acids
• circulating tumor cells
• exosomes
• hepatocellular carcinoma
• liquid biopsy
• personalized medicine

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

Bladder urothelial carcinoma (BLC) is one of the most common cancers in men, and its heterogeneity challenges the treatment to cure this disease. Recently, microRNAs (miRNAs) gained promising attention as biomarkers due to their potential roles in cancer biology. Identifying survival-associated miRNAs may help identify targets for therapeutic interventions in BLC. This work aims to identify a miRNA signature that could estimate the survival in patients with BLC. We developed a survival estimation method called BLC-SVR based on support vector regression incorporated with an optimal feature selection algorithm to select a robust set of miRNAs as a signature to estimate the survival in patients with BLC. BLC-SVR identified a miRNA signature consisting of 29 miRNAs and obtained a mean squared correlation coefficient and mean absolute error of 0.79 ± 0.02 and 0.52 ± 0.32 year between actual and estimated survival times, respectively. The prediction performance of BLC-SVR had a better estimation capability than other standard regression methods. In the identified miRNA signature, 14 miRNAs, hsa-miR-432-5p, hsa-let-7e-3p, hsa-miR-652-3p, hsa-miR-629-5p, and hsa-miR-203a-3p, hsa-miR-129-5p, hsa-miR-769-3p, hsa-miR-570-3p, hsa-miR-320c, hsa-miR-642a-5p, hsa-miR-496, hsa-miR-5480-3p, hsa-miR-221-5p, and hsa-miR-7-1-3p, were found to be good biomarkers for BLC diagnosis; and the six miRNAs, hsa-miR-652-5p, hsa-miR-193b-5p, hsa-miR-129-5p, hsa-miR-143-5p, hsa-miR-496, and hsa-miR-7-1-3p, were found to be good biomarkers of prognosis. Further bioinformatics analysis of this miRNA signature demonstrated its importance in various biological pathways and gene ontology annotation. The identified miRNA signature would further help in understanding of BLC diagnosis and prognosis in the development of novel miRNA-target based therapeutics in BLC.

• Bioinformatics
• Biomarkers
• Epigenetics
• Genetics
• Hepatocellular carcinoma
• In silico analysis
• Molecular pathways

Knowledge of the host-specific properties of the spike protein is of crucial importance to understand the adaptability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to infect multiple species and alter transmissibility, particularly in humans. Here, we propose a spike protein predictor SPIKES incorporating with an inheritable bi-objective combinatorial genetic algorithm to identify the biochemical properties of spike proteins and determine their specificity to human hosts. SPIKES identified 20 informative physicochemical properties of the spike protein, including information measures for alpha helix and relative mutability, and amino acid and dipeptide compositions, which have shown compositional difference at the amino acid sequence level between human and diverse animal coronaviruses. We suggest that alterations of these amino acids between human and animal coronaviruses may provide insights into the development and transmission of SARS-CoV-2 in human and other species and support the discovery of targeted antiviral therapies.

•

Differences exist in the amino acids within the S protein of diverse host species CoVs

• Association analysis
• Bioinformatics
• Virology

• Breast carcinoma
• CDC25A
• MiR-100-5p
• Migration and invasion
• Proliferation

Encapsulating the antisense oligonucleotide drug MK-ASODN with nanoliposomes greatly improved its potency and targeting to the heparin-binding growth factor midkine. The disposition and pharmacokinetic (PK) parameters of MK-ASODN nanoliposomes were studied in monkeys and rats, and the human PK parameters were predicted based on preclinical data using a physiologically based pharmacokinetic (PBPK) model. Following intravenous injection, the drug plasma concentration rapidly declined in a multiexponential manner, and the drug was rapidly transferred to tissues from the circulation. The terminal t_1/2 in plasma was clearly longer than that of the unmodified antisense nucleic acid drug. According to the AUC,MK-ASODN nanoliposomes were mainly distributed in the kidney, spleen, and liver. . MK-ASODN nanoliposomes were highly plasma protein bound, limiting their urinary excretion. Very little MK-ASODN nanoliposomes were detected in urine or feces. The plasma disposition of MK-ASODN nanoliposomes appeared nonlinear over the studied dose range of 11.5–46 mg kg⁻¹. The monkey PBPK model of MK-ASODN nanoliposomes was well established and successfully extrapolated to predict MK-ASODN nanoliposome PK in humans. These disposition and PK data support further development in phase I clinical studies.

• PBPK model
• antisense oligonucleotide drug
• hepatocellular carcinoma
• nanoliposome
• pharmacokinetics

• Evolutionary learning
• Hepatocellular carcinoma
• Machine learning
• Recurrence
• Surgery

Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine–based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.

• autism spectrum disorders
• feature selection
• gene expression profiles
• machine learning
• risk gene prediction

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.

A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e−08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.

A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

The online version contains supplementary material available at 10.1186/s12859-021-04189-2.

• Biomarkers
• Clear cell renal cell carcinoma
• Machine learning classifiers
• MicroRNAs
• Regularized cox model
• Survival analysis

• SARS-CoV-2 classification
• machine learning
• physicochemical properties
• support vector machines

• Amino Acid Sequence
• Animals
• COVID-19
• Chiroptera
• Humans
• SARS-CoV-2
• Spike Glycoprotein, Coronavirus

Ovarian cancer is a major gynaecological malignant tumor associated with a high mortality rate. Identifying survival-related variants may improve treatment and survival in patients with ovarian cancer. In this work, we proposed a support vector regression (SVR)-based method called OV-SURV, which is incorporated with an inheritable bi-objective combinatorial genetic algorithm for feature selection to identify a miRNA signature associated with survival in patients with ovarian cancer. There were 209 patients with miRNA expression profiles and survival information of ovarian cancer retrieved from The Cancer Genome Atlas database. OV-SURV achieved a mean correlation coefficient of 0.77±0.01and a mean absolute error of 0.69±0.02 years using 10-fold cross-validation. Analysis of the top ranked miRNAs revealed that the miRNAs, hsa-let-7f, hsa-miR-1237, hsa-miR-98, hsa-miR-933, and hsa-miR-889, were significantly associated with the survival in patients with ovarian cancer. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that four of these miRNAs, hsa-miR-182, hsa-miR-34a, hsa-miR-342, and hsa-miR-1304, were highly enriched in fatty acid biosynthesis, and the five miRNAs, hsa-let-7f, hsa-miR-34a, hsa-miR-342, hsa-miR-1304, and hsa-miR-24, were highly enriched in fatty acid metabolism. The prediction model with the identified miRNA signature consisting of prognostic biomarkers can benefit therapeutic decision making of ovarian cancer.

• machine learning
• miRNA signature
• ovarian cancer
• survival estimation

The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.

• LUAD
• LUSC
• early stage
• sex disparity
• transcriptomic biomarker