Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks.

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

Artificial Intelligence as Accelerator for Genomic Medicine and Planetary Health.

Genomic medicine has made important strides over the past several decades, but as new insights and technologies emerge, the applications of genomics in medicine and planetary health continue to evolve and expand. An important grand challenge is harnessing and making sense of the genomic big data in ways that best serve public and planetary health. Because human health is inextricably intertwined with the health of planetary ecosystems and nonhuman animals, genomic medicine is in need of high throughput bioinformatics analyses to harness and integrate human and ecological multiomics big data. It is in this overarching context that artificial intelligence (AI), particularly machine learning and deep learning, offers enormous potentials to advance genomic medicine in a spirit of One Health. This expert review offers an analysis of the rapidly emerging role of AI in genomic medicine, including its current drivers, levers, opportunities, and challenges. The scope of AI applications in genomic medicine is broad, ranging from efficient and automated data analysis to drug repurposing and precision medicine, as with its challenges such as veracity of the big data that AI sorely depends on, social biases that the AI-driven algorithms can introduce, and how best to incorporate AI with human intelligence. The road ahead for AI in genomic medicine is complex and arduous and yet worthy of cautious optimism as we face future pandemics and ecological crises in the 21st century. Now is a good time to think about the role of AI in genomic medicine and planetary health.

Systems-level biomarkers identification and drug repositioning in colorectal cancer.

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

Differential Protein Interactome in Esophageal Squamous Cell Carcinoma Offers Novel Systems Biomarker Candidates with High Diagnostic and Prognostic Performance.

Esophageal squamous cell carcinoma (ESCC) is among the most dangerous cancers with high mortality and lack of robust diagnostics and personalized/precision therapeutics. To achieve a systems-level understanding of tumorigenesis, unraveling of variations in the protein interactome and determination of key proteins exhibiting significant alterations in their interaction patterns during tumorigenesis are crucial. To this end, we have described differential protein-protein interactions and differentially interacting proteins (DIPs) in ESCC by utilizing the human protein interactome and transcriptome. Furthermore, DIP-centered modules were analyzed according to their potential in elucidation of disease mechanisms and improvement of efficient diagnostic, prognostic, and treatment strategies. Seven modules were presented as potential diagnostic, and 16 modules were presented as potential prognostic biomarker candidates. Importantly, our findings also suggest that 30 out of the 53 repurposed drugs were noncancer drugs, which could be used in the treatment of ESCC. Interestingly, 25 of these, proposed as novel drug candidates here, have not been previously associated in a context of esophageal cancer. In this context, risperidone and clozapine were validated for their growth inhibitory potential in three ESCC lines. Our findings offer a high potential for the development of innovative diagnostic, prognostic, and therapeutic strategies for further experimental studies in line with predictive diagnostics, targeted prevention, and personalization of medical services in ESCC specifically, and personalized cancer care broadly.

The Repertoire of Glycan Alterations and Glycoproteins in Human Cancers.

Cancer as the leading cause of death worldwide has many issues that still need to be addressed. Since the alterations on the glycan compositions or/and structures (i.e., glycosylation, sialylation, and fucosylation) are common features of tumorigenesis, glycomics becomes an emerging field examining the structure and function of glycans. In the past, cancer studies heavily relied on genomics and transcriptomics with relatively little exploration of the glycan alterations and glycoprotein biomarkers among individuals and populations. Since glycosylation of proteins increases their structural complexity by several orders of magnitude, glycome studies resulted in highly dynamic biomarkers that can be evaluated for cancer diagnosis, prognosis, and therapy. Glycome not only integrates our genetic background with past and present environmental factors but also offers a promise of more efficient patient stratification compared with genetic variations. Therefore, studying glycans holds great potential for better diagnostic markers as well as developing more efficient treatment strategies in human cancers. While recent developments in glycomics and associated technologies now offer new possibilities to achieve a high-throughput profiling of glycan diversity, we aim to give an overview of the current status of glycan research and the potential applications of the glycans in the scope of the personalized medicine strategies for cancer.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers.

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

Integrative transcriptomics analysis of lung epithelial cells and identification of repurposable drug candidates for COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease, more commonly COVID-19 has emerged as a world health pandemic. There are couples of treatment methods for COVID-19, however, well-established drugs and vaccines are urgently needed to treat the COVID-19. The new drug discovery is a tremendous challenge; repurposing of existing drugs could shorten the time and expense compared with de novo drug development. In this study, we aimed to decode molecular signatures and pathways of the host cells in response to SARS-CoV-2 and the rapid identification of repurposable drugs using bioinformatics and network biology strategies. We have analyzed available transcriptomic RNA-seq COVID-19 data to identify differentially expressed genes (DEGs). We detected 177 DEGs specific for COVID-19 where 122 were upregulated and 55 were downregulated compared to control (FDR<0.05 and logFC ≥ 1). The DEGs were significantly involved in the immune and inflammatory response. The pathway analysis revealed the DEGs were found in influenza A, measles, cytokine signaling in the immune system, interleukin-4, interleukin -13, interleukin -17 signaling, and TNF signaling pathways. Protein-protein interaction analysis showed 10 hub genes (BIRC3, ICAM1, IRAK2, MAP3K8, S100A8, SOCS3, STAT5A, TNF, TNFAIP3, TNIP1). The regulatory network analysis showed significant transcription factors (TFs) that target DEGs, namely FOXC1, GATA2, YY1, FOXL1, NFKB1. Finally, drug repositioning analysis was performed with these 10 hub genes and showed that in silico validated three drugs with molecular docking. The transcriptomics signatures, molecular pathways, and regulatory biomolecules shed light on candidate biomarkers and drug targets which have potential roles to manage COVID-19. ICAM1 and TNFAIP3 were the key hubs that have demonstrated good binding affinities with repurposed drug candidates. Dabrafenib, radicicol, and AT-7519 were the top-scored repurposed drugs that showed efficient docking results when they tested with hub genes. The identified drugs should be further evaluated in molecular level wet-lab experiments in prior to clinical studies in the treatment of COVID-19.

Drug Repositioning for P-Glycoprotein Mediated Co-Expression Networks in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most fatal types of cancers that is seen in both men and women. CRC is the third most common type of cancer worldwide. Over the years, several drugs are developed for the treatment of CRC; however, patients with advanced CRC can be resistant to some drugs. P-glycoprotein (P-gp) (also known as Multidrug Resistance 1, MDR1) is a well-identified membrane transporter protein expressed by ABCB1 gene. The high expression of MDR1 protein found in several cancer types causes chemotherapy failure owing to efflux drug molecules out of the cancer cell, decreases the drug concentration, and causes drug resistance. As same as other cancers, drug-resistant CRC is one of the major obstacles for effective therapy and novel therapeutic strategies are urgently needed. Network-based approaches can be used to determine specific biomarkers, potential drug targets, or repurposing approved drugs in drug-resistant cancers. Drug repositioning is the approach for using existing drugs for a new therapeutic purpose; it is a highly efficient and low-cost process. To improve current understanding of the MDR-1-related drug resistance in CRC, we explored gene co-expression networks around ABCB1 gene with different network sizes (50, 100, 150, 200 edges) and repurposed candidate drugs targeting the ABCB1 gene and its co-expression network by using drug repositioning approach for the treatment of CRC. The candidate drugs were also assessed by using molecular docking for determining the potential of physical interactions between the drug and MDR1 protein as a drug target. We also evaluated these four networks whether they are diagnostic or prognostic features in CRC besides biological function determined by functional enrichment analysis. Lastly, differentially expressed genes of drug-resistant (i.e., oxaliplatin, methotrexate, SN38) HT29 cell lines were found and used for repurposing drugs with reversal gene expressions. As a result, it is shown that all networks exhibited high diagnostic and prognostic performance besides the identification of various drug candidates for drug-resistant patients with CRC. All these results can shed light on the development of effective diagnosis, prognosis, and treatment strategies for drug resistance in CRC.

Higher proteotoxic stress rather than mitochondrial damage is involved in higher neurotoxicity of bortezomib compared to carfilzomib.

Proteasome inhibitors have great success for their therapeutic potential against hematologic malignancies. First generation proteasome inhibitor bortezomib induced peripheral neuropathy is considered as a limiting factor in chemotherapy and its second-generation counterpart carfilzomib is associated with lower rates of neurotoxicity. The mitochondrial toxicity (mitotoxicity) hypothesis arises from studies with animal models of bortezomib induced peripheral neuropathy. However, molecular mechanisms are not fully elucidated and the role of mitotoxicity in bortezomib and carfilzomib induced neurotoxicity has not been investigated comparatively. Herein, we characterized the neurotoxic effects of bortezomib and carfilzomib at the molecular level in human neuronal cells using LC-MS/MS analysis, flow cytometry, RT-qPCR, confocal microscopy and western blotting. We showed that bortezomib and carfilzomib affected the human neuronal proteome differently, and bortezomib caused higher proteotoxic stress via protein oxidation, protein K48-ubiquitination, heat shock protein expression upregulation and reduction of mitochondria membrane potential. Bortezomib and carfilzomib did not affect the gene expression levels related to mitochondrial dynamics (optic atrophy 1; OPA1, mitofusin 1; MFN1, mitofusin 2; MFN2, fission 1; FIS1, dynamin-related protein 1; DRP1) and overall mitophagy rate whereas, PINK1/Parkin mediated mitophagy gene expressions were altered with both drugs. Bortezomib and carfilzomib caused downregulation of the contents of mitochondrial oxidative phosphorylation complexes, voltage-dependent anion channel 1 (VDAC1) and uncoupling protein 2 (UCP2) similarly. Our findings suggest that, both drugs induce mitotoxicity besides proteotoxic stress in human neuronal cells and the higher incidence of neurotoxicity with bortezomib than carfilzomib is not directly related to mitochondrial pathways.

Pan-cancer mapping of differential protein-protein interactions.

Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.