Considerations for feature selection using gene pairs and applications in large-scale dataset integration, novel oncogene discovery, and interpretable cancer screening

Systematic analysis of the transcriptional landscape of melanoma reveals drug-target expression plasticity

Metastatic melanoma originates from melanocytes of the skin. Melanoma metastasis results in poor treatment prognosis for patients and is associated with epigenetic and transcriptional changes that reflect the developmental program of melanocyte differentiation from neural crest stem cells. Several studies have explored melanoma transcriptional heterogeneity using microarray, bulk and single-cell RNA-sequencing technologies to derive data-driven models of the transcriptional-state change which occurs during melanoma progression. No study has systematically examined how different models of melanoma progression derived from different data types, technologies and biological conditions compare. Here, we perform a cross-sectional study to identify averaging effects of bulk-based studies that mask and distort apparent melanoma transcriptional heterogeneity; we describe new transcriptionally distinct melanoma cell states, identify differential co-expression of genes between studies and examine the effects of predicted drug susceptibilities of different cell states between studies. Importantly, we observe considerable variability in drug-target gene expression between studies, indicating potential transcriptional plasticity of melanoma to down-regulate these drug targets and thereby circumvent treatment. Overall, observed differences in gene co-expression and predicted drug susceptibility between studies suggest bulk-based transcriptional measurements do not reliably gauge heterogeneity and that melanoma transcriptional plasticity is greater than described when studies are considered in isolation.

Transcriptomes reveal microRNAs and mRNAs in different photoperiods influencing cashmere growth in goat

Cashmere goat has a typical characteristic in seasonal growth of cashmere. Studies have shown that one of the main factors affecting the cyclical growth of the cashmere is the photoperiod, however, its molecular mechanism remains unclear. Inner Mongolia Arbas cashmere goat was used to reveal the mRNA-microRNA regulatory mechanisms of cashmere growth in different photoperiod. Skin samples from cashmere goats under light control (short photoperiod) and normal conditions (long photoperiod) were collected. Sequencing was performed after RNA extraction. The differentially expressed miRNA and mRNA expression profiles were successfully constructed. We found 56 significantly differentially expressed known mRNAs (P<0.01) and 14 microRNAs (P<0.05). The association analysis of the microRNAs and mRNAs showed that two differentially expressed miRNAs might be targeted by six differentially expressed genes. Targeting relationships of these genes and miRNAs are revealed and verified. In all, the light control technology provides a new way to promote cashmere growth. Our results provide some references in the cashmere growth and development.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

Transcriptome-Wide Analysis of Human Liver Reveals Age-Related Differences in the Expression of Select Functional Gene Clusters and Evidence for a PPP1R10-Governed 'Aging Cascade'

A transcriptome-wide analysis of human liver for demonstrating differences between young and old humans has not yet been performed. However, identifying major age-related alterations in hepatic gene expression may pinpoint ontogenetic shifts with important hepatic and systemic consequences, provide novel pharmacogenetic information, offer clues to efficiently counteract symptoms of old age, and improve the overarching understanding of individual decline. Next-generation sequencing (NGS) data analyzed by the Mann-Whitney nonparametric test and Ensemble Feature Selection (EFS) bioinformatics identified 44 transcripts among 60,617 total and 19,986 protein-encoding transcripts that significantly (p = 0.0003 to 0.0464) and strikingly (EFS score > 0.3:16 transcripts; EFS score > 0.2:28 transcripts) differ between young and old livers. Most of these age-related transcripts were assigned to the categories 'regulome', 'inflammaging', 'regeneration', and 'pharmacogenes'. NGS results were confirmed by quantitative real-time polymerase chain reaction. Our results have important implications for the areas of ontogeny/aging and the age-dependent increase in major liver diseases. Finally, we present a broadly substantiated and testable hypothesis on a genetically governed 'aging cascade', wherein PPP1R10 acts as a putative ontogenetic master regulator, prominently flanked by IGFALS and DUSP1. This transcriptome-wide analysis of human liver offers potential clues towards developing safer and improved therapeutic interventions against major liver diseases and increased insights into key mechanisms underlying aging.

Elevated expression of NFE2L3 promotes the development of gastric cancer through epithelial-mesenchymal transformation

Gastric cancer (GC) is a malignant tumor with high mortality, but research on its molecular mechanisms remain limited. This study is the first to explore the biological role of nuclear factor NFE2L3 (nuclear factor, erythroid 2 like 3) in GC. We used Western blot and RT–qPCR to detect gene expression at the protein or mRNA level. Short hairpin RNA (shRNA) transfection was used to inhibit NFE2L3 expression. CCK-8 and colony formation assays were used to detect cell proliferation. Cell migration, invasion, cell cycle and apoptosis were detected by Transwell assays and flow cytometry. The results showed that NFE2L3 was highly expressed in gastric cancer tissues and promoted gastric cancer cell proliferation and metastasis. Inhibiting NFE2L3 expression blocks the cell cycle and increases the proportion of apoptotic cells, whereas NFE2L3 expression promotes the epithelial-mesenchymal transformation (EMT) process. In summary, NFE2L3 is highly expressed in gastric cancer and promotes gastric cancer cell proliferation and metastasis and the EMT process.

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

BACKGROUND

Today, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited.

METHODS

In this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). The decision-making strategy for the proposed ensemble machine is based on the aggregation of the predicted scores obtained from individual learning classifiers to be prioritized homo sapiens genes annotated as protein-coding from NCBI.

RESULTS

This study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions are discussed based on gene set enrichment analysis. Third, statistical validation and comparison of all learning methods are performed by some evaluation metrics. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR < 0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA. It includes HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reaches 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case.

CONCLUSIONS

This research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing. The schematic representation of the proposed model is presented as the Graphic abstract.