Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer.

Background: Lung cancer is the second most common cancer and the main leading cause of cancer-associated death worldwide. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancer diagnoses and more than 50% of all lung cancer cases are diagnosed at an advanced stage; hence have poor prognosis. Therefore, it is important to diagnose NSCLC patients reliably and as early as possible in order to reduce the risk of mortality. Methods: We identified blood-based gene markers for early NSCLC by performing a multi-omics approach utilizing integrated analysis of global gene expression and copy number alterations of NSCLC patients using array-based techniques. We also validated the diagnostic and the prognostic potential of the gene signature using independent datasets with detailed clinical information. Results: We identified 12 genes that are significantly expressed in NSCLC patients' blood, at the earliest stages of the disease, and associated with a poor disease outcome. We then validated 12-gene signature's diagnostic and prognostic value using independent datasets of gene expression profiling of over 1000 NSCLC patients. Indeed, 12-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis (HR = 2.64, 95% CI = 1.72-4.07; p = 1.3 × 10-8). Significantly altered functions, pathways, and gene networks revealed alterations in several key genes and cancer-related pathways that may have importance for NSCLC transformation, including FAM83A, ZNF696, UBE2C, RECK, TIMM50, GEMIN7, and XPO5. Conclusion: Our findings suggest that integrated genomic and network analyses may provide a reliable approach to identify genes that are associated with NSCLC, and lead to improved diagnosis detecting the disease in early stages in patients' blood instead of using invasive techniques and also have prognostic potential for discriminating high-risk patients from the low-risk ones.

Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer.

Background: Colorectal cancer (CRC) is the third most common cancer and third leading cause of cancer-associated deaths worldwide. Diagnosing CRC patients reliably at an early and curable stage is of utmost importance to reduce the risk of mortality. Methods: We identified global differentially expressed genes with copy number alterations in patients with CRC. We then identified genes that are also expressed in blood, which resulted in a blood-based gene signature. We validated the gene signature's diagnostic and prognostic potential using independent datasets of gene expression profiling from over 800 CRC patients with detailed clinical data. Functional enrichment, gene interaction networks and pathway analyses were also performed. Results: The analysis revealed a 17-gene signature that is expressed in blood and demonstrated that it has diagnostic potential. The 17-gene SVM classifier displayed 99 percent accuracy in predicting the patients with CRC. Moreover, we developed a prognostic model and defined a risk-score using 17-gene and validated that high risk score is strongly associated with poor disease outcome. The 17-gene signature predicted disease outcome independent of other clinical factors in the multivariate analysis (HR = 2.7, 95% CI = 1.3-5.3, p = 0.005). In addition, our gene network and pathway analyses revealed alterations in oxidative stress, STAT3, ERK/MAPK, interleukin and cytokine signaling pathways as well as potentially important hub genes, including BCL2, MS4A1, SLC7A11, AURKA, IL6R, TP53, NUPR1, DICER1, DUSP5, SMAD3, and CCND1. Conclusion: Our results revealed alterations in various genes and cancer-related pathways that may be essential for CRC transformation. Moreover, our study highlights diagnostic and prognostic value of our gene signature as well as its potential use as a blood biomarker as a non-invasive diagnostic method. Integrated analysis transcriptomic data coupled with copy number aberrations may provide a reliable method to identify key biological programs associated with CRC and lead to improved diagnosis and therapeutic options.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer.

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses.

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature's ability to detect the disease in its early stages in patients' biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

Mutation in FBXO32 causes dilated cardiomyopathy through up-regulation of ER-stress mediated apoptosis.

Endoplasmic reticulum (ER) stress induction of cell death is implicated in cardiovascular diseases. Sustained activation of ER-stress induces the unfolded protein response (UPR) pathways, which in turn activate three major effector proteins. We previously reported a missense homozygous mutation in FBXO32 (MAFbx, Atrogin-1) causing advanced heart failure by impairing autophagy. In the present study, we performed transcriptional profiling and biochemical assays, which unexpectedly revealed a reduced activation of UPR effectors in patient mutant hearts, while a strong up-regulation of the CHOP transcription factor and of its target genes are observed. Expression of mutant FBXO32 in cells is sufficient to induce CHOP-associated apoptosis, to increase the ATF2 transcription factor and to impair ATF2 ubiquitination. ATF2 protein interacts with FBXO32 in the human heart and its expression is especially high in FBXO32 mutant hearts. These findings provide a new underlying mechanism for FBXO32-mediated cardiomyopathy, implicating abnormal activation of CHOP. These results suggest alternative non-canonical pathways of CHOP activation that could be considered to develop new therapeutic targets for the treatment of FBXO32-associated DCM.

Expression profiling of WD40 family genes including DDB1- and CUL4- associated factor (DCAF) genes in mice and human suggests important regulatory roles in testicular development and spermatogenesis.

BACKGROUND: The WD40-repeat containing proteins, including DDB1-CUL4-associated factors (DCAFs), are abundant and conserved proteins that play important roles in different cellular processes including spermatogenesis. DCAFs are subset of WD40 family proteins that contain WDxR motif and have been proposed to function as substrate receptor for Cullin4-RING-based E3 ubiquitin ligase complexes to recruit diverse proteins for ubiquitination, a vital process in spermatogenesis. Large number of WD40 genes has been identified in different species including mouse and human. However, a systematic expression profiling of WD40 genes in different tissues of mouse and human has not been investigated. We hypothesize that large number of WD40 genes may express highly or specifically in the testis, where their expression is uniquely regulated during testis development and spermatogenesis. Therefore, the objective of this study is to mine and characterize expression patterns of WD40 genes in different tissues of mouse and human with particular emphasis on DCAF genes expressions during mouse testicular development. RESULTS: Publically available RNA sequencing (RNA seq) data mining identified 347 and 349 WD40 genes in mouse and human, respectively. Hierarchical clustering and heat map analyses of RNA seq datasets revealed differential expression patterns of WD40 genes with around 60-73% of the genes were highly or specifically expressed in testis. Similarly, around 74-83% of DCAF genes were predominantly or specifically expressed in testis. Moreover, WD40 genes showed distinct expression patterns during embryonic and postnatal testis development in mice. Finally, different germ cell populations of testis showed specific patterns of WD40 genes expression. Predicted gene ontology analyses revealed more than 80% of these proteins are implicated in cellular, metabolic, biological regulation and cell localization processes. CONCLUSIONS: We have identified large number of WD40 family genes that are highly or specifically expressed in the testes of mouse and human. Moreover, WD40 genes have distinct expression patterns during embryonic and postnatal development of the testis in mice. Further, different germ cell populations within the testis showed specific patterns of WD40 genes expression. These results provide foundation for further research towards understanding the functional genomics and molecular mechanisms of mammalian testis development and spermatogenesis.

Publisher Correction: RNA-Seq transcriptome profiling in three liver regeneration models in rats: comparative analysis of partial hepatectomy, ALLPS, and PVL.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

RNA-Seq transcriptome profiling in three liver regeneration models in rats: comparative analysis of partial hepatectomy, ALLPS, and PVL.

The liver is a unique organ that has a phenomenal capacity to regenerate after injury. Different surgical procedures, including partial hepatectomy (PH), intraoperative portal vein ligation (PVL), and associated liver partition and portal vein ligation for staged hepatectomy (ALPPS) show clinically distinct recovery patterns and regeneration. The observable clinical differences likely mirror some underlying variations in the patterns of gene activation and regeneration pathways. In this study, we provided a comprehensive comparative transcriptomic analysis of gene regulation in regenerating rat livers temporally spaced at 24 h and 96 h after PH, PVL, and ALPPS. The time-dependent factors appear to be the most important determinant of post-injury alterations of gene expression in liver regeneration. Gene expression profile after ALPPS showed more similar expression pattern to the PH than the PVL at the early phase of the regeneration. Early transcriptomic changes and predicted upstream regulators that were found in all three procedures included cell cycle associated genes (E2F1, CCND1, FOXM1, TP53, and RB1), transcription factors (Myc, E2F1, TBX2, FOXM1), DNA replication regulators (CDKN1A, EZH2, RRM2), G1/S-transition regulators (CCNB1, CCND1, RABL6), cytokines and growth factors (CSF2, IL-6, TNF, HGF, VEGF, and EGF), ATM and p53 signaling pathways. The functional pathway, upstream, and network analyses revealed both unique and overlapping molecular mechanisms and pathways for each surgical procedure. Identification of molecular signatures and regenerative signaling pathways for each surgical procedure further our understanding of key regulators of liver regeneration as well as patient populations that are likely to benefit from each procedure.

Rett Syndrome, a Neurodevelopmental Disorder, Whole-Transcriptome, and Mitochondrial Genome Multiomics Analyses Identify Novel Variations and Disease Pathways.

Rett syndrome (RTT) is a severe neurodevelopmental disorder reported worldwide in diverse populations. RTT is diagnosed primarily in females, with clinical findings manifesting early in life. Despite the variable rates across populations, RTT has an estimated prevalence of ∼1 in 10,000 live female births. Among 215 Saudi Arabian patients with neurodevelopmental and autism spectrum disorders, we identified 33 patients with RTT who were subsequently examined by genome-wide transcriptome and mitochondrial genome variations. To the best of our knowledge, this is the first in-depth molecular and multiomics analyses of a large cohort of Saudi RTT cases with a view to informing the underlying mechanisms of this disease that impact many patients and families worldwide. The patients were unrelated, except for 2 affected sisters, and comprised of 25 classic and eight atypical RTT cases. The cases were screened for methyl-CpG binding protein 2 (MECP2), CDKL5, FOXG1, NTNG1, and mitochondrial DNA (mtDNA) variants, as well as copy number variations (CNVs) using a genome-wide experimental strategy. We found that 15 patients (13 classic and two atypical RTT) have MECP2 mutations, 2 of which were novel variants. Two patients had novel FOXG1 and CDKL5 variants (both atypical RTT). Whole mtDNA sequencing of the patients who were MECP2 negative revealed two novel mtDNA variants in two classic RTT patients. Importantly, the whole-transcriptome analysis of our RTT patients' blood and further comparison with previous expression profiling of brain tissue from patients with RTT revealed 77 significantly dysregulated genes. The gene ontology and interaction network analysis indicated potentially critical roles of MAPK9, NDUFA5, ATR, SMARCA5, RPL23, SRSF3, and mitochondrial dysfunction, oxidative stress response and MAPK signaling pathways in the pathogenesis of RTT genes. This study expands our knowledge on RTT disease networks and pathways as well as presents novel mutations and mtDNA alterations in RTT in a population sample that was not previously studied.

New Insights into the Impact of Genome-Wide Copy Number Variations on Complex Congenital Heart Disease in Saudi Arabia.

Congenital heart diseases (CHDs) are complex traits that manifest in diverse clinical phenotypes such as the Tetralogy of Fallot (TOF), valvular and ventricular/atrial septal defects. Genetic mechanisms of CHDs have remained largely unclear to date. Copy number variations (CNVs) have been implicated in many complex diseases but their impact has not been examined extensively in various forms of CHD lesions. We report in this study, to the best of our knowledge, the largest cohort of Saudi Arab CHD patients to date who were evaluated using genome-wide CNV analysis. In a sample of 134 Saudi Arab patients with CHD, 66 exhibited pathogenic or likely pathogenic CNVs. Notably, 21 copy number gains and 11 copy number losses were detected that encompassed 141 genes and 146 genes, respectively. The most frequent gains were on 17q21.31, 8p11.21, and 22q11.23, whereas the losses were primarily localized to 16p11.2. Interestingly, all lesions have had gains at 17q21.31. Septal defects had also gains at 8p11.21 and 22q11.23, valvular lesions at 8p11.21, 22q11.23, and 2q13, and TOF at 16p11.2. Functional and network analyses demonstrated that cardiovascular and nervous system development and function as well as cell death/survival were most significantly associated with CNVs, thus highlighting the potentially important genes likely to be involved in CHD, including NPHP1, PLCB1, KANSL1, and NR3C1. In conclusion, this genome-wide analysis identifies a high frequency of CNVs mostly in patients with septal defects, primarily influencing cardiovascular developmental and functional pathways, thereby offering a deeper insight into the complex networks involved in CHD pathogenesis.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review.

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

PD-L1 promotes OCT4 and Nanog expression in breast cancer stem cells by sustaining PI3K/AKT pathway activation.

The expression of PD-L1 in breast cancer is associated with estrogen receptor negativity, chemoresistance and epithelial-to-mesenchymal transition (EMT), all of which are common features of a highly tumorigenic subpopulation of cancer cells termed cancer stem cells (CSCs). Hitherto, the expression and intrinsic role of PD-L1 in the dynamics of breast CSCs has not been investigated. To address this issue, we used transcriptomic datasets, proteomics and several in vitro and in vivo assays. Expression profiling of a large breast cancer dataset (530 patients) showed statistically significant correlation (p < 0.0001, r = 0.36) between PD-L1 expression and stemness score of breast cancer. Specific knockdown of PD-L1 using ShRNA revealed its critical role in the expression of the embryonic stem cell transcriptional factors: OCT-4A, Nanog and the stemness factor, BMI1. Conversely, these factors could be induced upon PD-L1 ectopic expression in cells that are normally PD-L1 negative. Global proteomic analysis hinted for the central role of AKT in the biology of PD-L1 expressing cells. Indeed, PD-L1 positive effect on OCT-4A and Nanog was dependent on AKT activation. Most importantly, downregulation of PD-L1 compromised the self-renewal capability of breast CSCs in vitro and in vivo as shown by tumorsphere formation assay and extreme limiting dilution assay, respectively. This study demonstrates a novel role for PD-L1 in sustaining stemness of breast cancer cells and identifies the subpopulation and its associated molecular pathways that would be targeted upon anti-PD-L1 therapy.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network.

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Bidirectional crosstalk between PD-L1 expression and epithelial to mesenchymal transition: significance in claudin-low breast cancer cells.

BACKGROUND: The T-cell inhibitory molecule PD-L1 (B7-H1, CD274) is expressed on tumor cells of a subset of breast cancer patients. However, the mechanism that regulates PD-L1 expression in this group of patients is still not well-identified. METHODS: We have used loss and gain of function gene manipulation approach, multi-parametric flow cytometry, large scale gene expression dataset analysis and immunohistochemistry of breast cancer tissue sections. RESULTS: Induction of epithelial to mesenchymal transition (EMT) in human mammary epithelial cells upregulated PD-L1 expression, which was dependent mainly on the activation of the PI3K/AKT pathway. Interestingly, gene expression signatures available from large cohort of breast tumors showed a significant correlation between EMT score and the PD-L1 mRNA level (p < 0.001). Strikingly, very strong association (p < 0.0001) was found between PD-L1 expression and claudin-low subset of breast cancer, which is known to have high EMT score. On the protein level, significant correlation was found between PD-L1 expression and standard markers of EMT (p = 0.005) in 67 breast cancer patients. Importantly, specific downregulation of PD-L1 in claudin-low breast cancer cells showed signs of EMT reversal as manifested by CD44 and Vimentin downregulation and CD24 upregulation. CONCLUSIONS: We have demonstrated a bidirectional effect between EMT status and PD-L1 expression especially in claudin-low subtype of breast cancer cells. Our findings highlights the potential dual benefit of anti-PD-L1 particularly in this subset of breast cancer patients that will likely benefit more from anti-PD-L1 targeted therapy as well as in monitoring biological changes upon treatment.

ISCA2 mutation causes infantile neurodegenerative mitochondrial disorder.

BACKGROUND: There are numerous nuclear genes that cause mitochondrial disorders and clinically and genetically heterogeneous disorders whose aetiology often remains unsolved. In this study, we aim to investigate an autosomal recessive syndrome causing leukodystrophy and neuroregression. We studied six patients from five unrelated consanguineous families. METHODS: Patients underwent full neurological, radiological, genetic, metabolic and dysmorphological examinations. Exome sequencing coupled with autozygosity mapping, Sanger sequencing, microsatellite haplotyping, standard and molecular karyotyping and whole mitochondrial DNA sequencing were used to identify the genetic cause of the syndrome. Immunohistochemistry, transmission electron microscopy, confocal microscopy, dipstick assays, quantitative PCR, reverse transcription PCR and quantitative reverse transcription PCR were performed on different tissue samples from the patients. RESULTS: We identified a homoallelic missense founder mutation in ISCA2 leading to mitochondrial depletion and reduced complex I activity as well as decreased ISCA2, ISCA1 and IBA57 expression in fibroblasts. MRI indicated similar white matter abnormalities in the patients. Histological examination of the skeletal muscle showed mild to moderate variation in myofibre size and the presence of many randomly distributed atrophic fibres. CONCLUSIONS: Our data demonstrate that ISCA2 deficiency leads to a hereditary mitochondrial neurodegenerative white matter disease in infancy.