Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer

Maternal Methyl-Enriched Diet Increases DNMT1, HCN1, and TH Gene Expression and Suppresses Absence Seizures and Comorbid Depression in Offspring of WAG/Rij Rats

The reduced expression of the HCN1 ion channel in the somatosensory cortex (SSC) and mesolimbic dopamine deficiency are thought to be associated with the genesis of spike-wave discharges (SWDs) and comorbid depression in the WAG/Rij rat model of absence epilepsy. This study aimed to investigate whether the maternal methyl-enriched diet (MED), which affects DNA methylation, can alter DNMT1, HCN1, and TH gene expression and modify absence seizures and comorbid depression in WAG/Rij offspring. WAG/Rij mothers were fed MED (choline, betaine, folic acid, vitamin B12, L-methionine, zinc) or a control diet for a week before mating, during pregnancy, and for a week after parturition. MED caused sustained suppression of SWDs and symptoms of comorbid depression in the offspring. Disease-modifying effects of MED were associated with increased expression of the DNMT1 and HCN1 genes in the SSC and hippocampus, as well as DNMT1, HCN1, and TH genes in the nucleus accumbens. No changes in gene expression were detected in the hypothalamus. The results indicate that maternal MED can suppress the genetic absence epilepsy and comorbid depression in offspring. Increased expression of the DNMT1, HCN1, and TH genes is suggested to be a molecular mechanism of this beneficial phenotypic effect.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Multi-Omics Immune Interaction Networks in Lung Cancer Tumorigenesis, Proliferation, and Survival

There are currently no effective biomarkers for prognosis and optimal treatment selection to improve non-small cell lung cancer (NSCLC) survival outcomes. This study further validated a seven-gene panel for diagnosis and prognosis of NSCLC using RNA sequencing and proteomic profiles of patient tumors. Within the seven-gene panel, ZNF71 expression combined with dendritic cell activities defined NSCLC patient subgroups (n = 966) with distinct survival outcomes (p = 0.04, Kaplan-Meier analysis). ZNF71 expression was significantly associated with the activities of natural killer cells (p = 0.014) and natural killer T cells (p = 0.003) in NSCLC patient tumors (n = 1016) using Chi-squared tests. Overexpression of ZNF71 resulted in decreased expression of multiple components of the intracellular intrinsic and innate immune systems, including dsRNA and dsDNA sensors. Multi-omics networks of ZNF71 and the intracellular intrinsic and innate immune systems were computed as relevant to NSCLC tumorigenesis, proliferation, and survival using patient clinical information and in-vitro CRISPR-Cas9/RNAi screening data. From these networks, pan-sensitive and pan-resistant genes to 21 NCCN-recommended drugs for treating NSCLC were selected. Based on the gene associations with patient survival and in-vitro CRISPR-Cas9, RNAi, and drug screening data, MEK1/2 inhibitors PD-198306 and U-0126, VEGFR inhibitor ZM-306416, and IGF-1R inhibitor PQ-401 were discovered as potential targeted therapy that may also induce an immune response for treating NSCLC.

Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients. Boolean implication networks were used to construct multimodal networks using patient DNA copy number variation, mRNA, and protein expression profiles. T statistics of differential gene/protein expression in tumors versus non-cancerous adjacent tissues, dependency scores in in vitro CRISPR-Cas9/RNA interference (RNAi) screening of human NSCLC cell lines, and hazard ratios in univariate Cox modeling of the Cancer Genome Atlas (TCGA) NSCLC patients were correlated with graph theory centrality metrics. Hub genes in multi-omics networks involving gene/protein expression were associated with oncogenic, proliferative potentials and poor patient survival outcomes (p < 0.05, Pearson's correlation). Immunotherapy targets PD1, PDL1, CTLA4, and CD27 were ranked as top hub genes within the 10th percentile in most constructed multi-omics networks. BUB3, DNM1L, EIF2S1, KPNB1, NMT1, PGAM1, and STRAP were discovered as important hub genes in NSCLC proliferation with oncogenic potential. These results support the importance of hub genes in NSCLC tumorigenesis, proliferation, and prognosis, with implications in prioritizing therapeutic targets to improve patient survival outcomes.

Expression Analysis of Genes Involved in Transport Processes in Mice with MPTP-Induced Model of Parkinson’s Disease

Processes of intracellular and extracellular transport play one of the most important roles in the functioning of cells. Changes to transport mechanisms in a neuron can lead to the disruption of many cellular processes and even to cell death. It was shown that disruption of the processes of vesicular, axonal, and synaptic transport can lead to a number of diseases of the central nervous system, including Parkinson’s disease (PD). Here, we studied changes in the expression of genes whose protein products are involved in the transport processes (Snca, Drd2, Rab5a, Anxa2, and Nsf) in the brain tissues and peripheral blood of mice with MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced models of PD. We detected changes in the expressions of Drd2, Anxa2, and Nsf at the earliest modeling stages. Additionally, we have identified conspicuous changes in the expression level of Anxa2 in the striatum and substantia nigra of mice with MPTP-induced models of PD in its early stages. These data clearly suggest the involvement of protein products in these genes in the earliest stages of the pathogenesis of PD.

A Multi-Omics Network of a Seven-Gene Prognostic Signature for Non-Small Cell Lung Cancer

There is an unmet clinical need to identify patients with early-stage non-small cell lung cancer (NSCLC) who are likely to develop recurrence and to predict their therapeutic responses. Our previous study developed a qRT-PCR-based seven-gene microfluidic assay to predict the recurrence risk and the clinical benefits of chemotherapy. This study showed it was feasible to apply this seven-gene panel in RNA sequencing profiles of The Cancer Genome Atlas (TCGA) NSCLC patients (n = 923) in randomly partitioned feasibility-training and validation sets (p < 0.05, Kaplan-Meier analysis). Using Boolean implication networks, DNA copy number variation-mediated transcriptional regulatory network of the seven-gene signature was identified in multiple NSCLC cohorts (n = 371). The multi-omics network genes, including PD-L1, were significantly correlated with immune infiltration and drug response to 10 commonly used drugs for treating NSCLC. ZNF71 protein expression was positively correlated with epithelial markers and was negatively correlated with mesenchymal markers in NSCLC cell lines in Western blots. PI3K was identified as a relevant pathway of proliferation networks involving ZNF71 and its isoforms formulated with CRISPR-Cas9 and RNA interference (RNAi) profiles. Based on the gene expression of the multi-omics network, repositioning drugs were identified for NSCLC treatment.

A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer

In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.

Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer

To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. Major immune-checkpoint inhibitors (ICIs) have more DNA copy number variations (CNV) than mutations in The Cancer Genome Atlas (TCGA) NSCLC tumors. Nevertheless, CNV-mediated dysregulated gene expression in NSCLC is not well understood. Integrated CNV and transcriptional profiles in NSCLC tumors (n = 371) were analyzed using Boolean implication networks for the identification of a multi-omics CD27, PD1, and PDL1 network, containing novel prognostic genes and proliferation genes. A 5-gene (EIF2AK3, F2RL3, FOSL1, SLC25A26, and SPP1) prognostic model was developed and validated for patient stratification (p < 0.02, Kaplan-Meier analyses) in NSCLC tumors (n = 1163). A total of 13 genes (COPA, CSE1L, EIF2B3, LSM3, MCM5, PMPCB, POLR1B, POLR2F, PSMC3, PSMD11, RPL32, RPS18, and SNRPE) had a significant impact on proliferation in 100% of the NSCLC cell lines in both CRISPR-Cas9 (n = 78) and RNA interference (RNAi) assays (n = 92). Multiple identified genes were associated with chemoresponse and radiotherapy response in NSCLC cell lines (n = 117) and patient tumors (n = 966). Repurposing drugs were discovered based on this immune-omics network to improve NSCLC treatment.

Preparation of a phosphotyrosine-protein standard for use in semiquantitative western blotting with enhanced chemiluminescence

Protein tyrosine phosphorylation is key to activation of receptor tyrosine kinases (RTK) that drive development of some cancers. One challenge of RTK-targeted therapy is identification of those tumors that express non-mutated but activated RTKs. Phosphotyrosine (pTyr) RTK levels should be more predictive of the latter than expressed total protein. Western blotting (WB) with a pTyr antibody and enhanced chemiluminescence (ECL) detection is sufficiently sensitive to detect pTyr-RTKs in human tumor homogenates. Presentation of results by comparing WB images, however, is wanting. Here we describe the preparation of a new pTyr-protein standard, pTyr-ALK48-SB (pA), derived from a commercial anaplastic lymphoma kinase (ALK) recombinant fragment, and its use to quantify pTyr-epidermal growth factor receptor (pTyr-EGFR) in commercial A431 cell lysates. Linearity of one-dimensional (1D) WB plots of pA band density versus load as well as its lower level of detection (0.1 ng, 2 fmole) were determined for standardized conditions. Adding pA to two lots of A431 cell lysates with high and low pTyr-EGFR allowed normalization and quantification of the latter by expressing results as density ratios for both 1D and 2D WB. This approach is semi-quantitative because unknown RTKs may be outside the linear range of detection. Semiquantitative ratios are an improvement over comparisons of images without a reference standard and facilitate comparisons between samples.

An Introduction to the Analysis of Single-Cell RNA-Sequencing Data

The recent development of single-cell RNA sequencing has deepened our understanding of the cell as a functional unit, providing new insights based on gene expression profiles of hundreds to hundreds of thousands of individual cells, and revealing new populations of cells with distinct gene expression profiles previously hidden within analyses of gene expression performed on bulk cell populations. However, appropriate analysis and utilization of the massive amounts of data generated from single-cell RNA sequencing experiments are challenging and require an understanding of the experimental and computational pathways taken between preparation of input cells and output of interpretable data. In this review, we will discuss the basic principles of these new technologies, focusing on concepts important in the analysis of single-cell RNA-sequencing data. Specifically, we summarize approaches to quality-control measures for determination of which single cells to include for further examination, methods of data normalization and scaling to overcome the relatively inefficient capture rate of mRNA from each cell, and clustering and visualization algorithms used for dimensional reduction of the data to a two-dimensional plot.