Estimates and Forecasts on the Burden of Prediabetes and Diabetes in Adult and Elderly Population in Turkiye.

AIMS: Diabetes mellitus is a chronic disease that limits the quality and duration of life. We aimed to estimate the impact of demographic change on the burden of prediabetes and diabetes between 2010 and 2021, and the projections to 2030 and 2045 in Turkiye. MATERIALS AND METHODS: Prediabetes and diabetes estimates were calculated by direct standardization method using age- and sex-specific prevalence data from the previous 'Turkish Epidemiology Survey of Diabetes, Hypertension, Obesity and Endocrine Disease' (TURDEP-II) as reference. The 2010-2021 population demographics were obtained from TurkStat. Comparative age-adjusted diabetes prevalence was estimated using the standard population models of world and Europe. RESULTS: Estimates depicted that the population (20-84 years) of any degree of glucose intolerance in Turkiye increased by over 5.7 million (diabetes: 2.4 million and prediabetes: 3.3 million) from 2010 to 2021. While the increase in prediabetes and diabetes prevalence was 24.3% and 35.2% in overall population, corresponding increase were 46.5% and 51.3% in the elderly. Estimated prevalence of prediabetes and diabetes in 2021 was significantly higher in women than in men (prediabetes: 32.6% vs. 25.2%; diabetes: 17.1% vs. 14.2%). The comparative age-adjusted diabetes prevalence to the European population model was higher than that of the world population model (19.4% vs. 15.0%). According to the projections the prevalence of diabetes will reach 17.5% in 2030 and 19.2% in 2045. CONCLUSION: Assuming age- and sex-specific diabetes prevalence of TURDEP-II survey remained constant, this study revealed that the number of people with diabetes in the general population (particularly in the elderly) in the last 11 years in Turkiye has increased in parallel with the population growth and aging; it will continue to grow over the coming decades. This means the burden of diabetes on the social, economic and health services will remain to increase. The fact suggests that there is an urgent need for re-organization of care as well as to develop and implement a country-specific prevention program to reduce this burden.

Potential Early Markers for Breast Cancer: A Proteomic Approach Comparing Saliva and Serum Samples in a Pilot Study.

Breast cancer is the second leading cause of death for women in the United States, and early detection could offer patients the opportunity to receive early intervention. The current methods of diagnosis rely on mammograms and have relatively high rates of false positivity, causing anxiety in patients. We sought to identify protein markers in saliva and serum for early detection of breast cancer. A rigorous analysis was performed for individual saliva and serum samples from women without breast disease, and women diagnosed with benign or malignant breast disease, using isobaric tags for relative and absolute quantitation (iTRAQ) technique, and employing a random effects model. A total of 591 and 371 proteins were identified in saliva and serum samples from the same individuals, respectively. The differentially expressed proteins were mainly involved in exocytosis, secretion, immune response, neutrophil-mediated immunity and cytokine-mediated signaling pathway. Using a network biology approach, significantly expressed proteins in both biological fluids were evaluated for protein-protein interaction networks and further analyzed for these being potential biomarkers in breast cancer diagnosis and prognosis. Our systems approach illustrates a feasible platform for investigating the responsive proteomic profile in benign and malignant breast disease using saliva and serum from the same women.

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.

Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis.

Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein⁻protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan⁻Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

A Transcriptomic and Reverse-Engineering Strategy Reveals Molecular Signatures of Arachidonic Acid Metabolism in 12 Cancers.

Cancer and arachidonic acid (AA) have important linkages. For example, AA metabolites regulate several critical biological functions associated with carcinogenesis: angiogenesis, apoptosis, and cancer invasion. However, little is known about the comparative changes in metabolite expression of the arachidonic acid pathway (AAP) in carcinogenesis. In this study, we examined transcriptome data from 12 cancers, such as breast invasive carcinoma, colon adenocarcinoma, lung adenocarcinoma, and prostate adenocarcinoma. We also report here a reverse-engineering strategy wherein we estimated metabolic signatures associated with AAP by (1) making deductive inferences through transcriptome-level data extraction, (2) remodeling AA metabolism, and (3) performing a comparative analysis of cancer types to determine the similarities and differences between different cancer types with respect to AA metabolic alterations. We identified 77 AAP gene signatures differentially expressed in cancers and 37 AAP metabolites associated with them. Importantly, the metabolite 15(S)-HETE was identified in almost all cancers, while arachidonate, 5-HETE, PGF2α, 14,15-EET, 8,9-EET, 5,6-EET, and 20-HETE were discovered as other most regulated metabolites. This study shows that the 12 cancers studied herein, although in different branches of the AAP, have altered expression of AAP gene signatures. Going forward, AA related-cancer research generally, and the molecular signatures and their estimated metabolites reported herein specifically, hold broad promise for precision/personalized medicine in oncology as potential therapeutic and diagnostic targets.

Precision Diagnosis of Maturity-Onset Diabetes of the Young with Next-Generation Sequencing: Findings from the MODY-IST Study in Adult Patients.

Maturity-onset diabetes of the young (MODY) is a highly heterogeneous group of monogenic and nonautoimmune diseases. Misdiagnosis of MODY is a widespread problem and about 5% of patients with type 2 diabetes mellitus and nearly 10% with type 1 diabetes mellitus may actually have MODY. Using next-generation DNA sequencing (NGS) to facilitate accurate diagnosis of MODY, this study investigated mutations in 13 MODY genes (HNF4A, GCK, HNF1A, PDX1, HNF1B, NEUROD1, KLF11, CEL, PAX4, INS, BLK, ABCC8, and KCNJ11). In addition, we comprehensively investigated the clinical phenotypic effects of the genetic variations identified. Fifty-one adult patients with suspected MODY and 64 healthy controls participated in the study. We identified 7 novel and 10 known missense mutations localized in PDX1, HNF1B, KLF11, CEL, BLK, and ABCC8 genes in 29.4% of the patient sample. Importantly, we report several mutations that were classified as "deleterious" as well as those predicted as "benign." Notably, the ABCC8 p.R1103Q, ABCC8 p.V421I, CEL I336T, CEL p.N493H, BLK p.L503P, HNF1B p.S362P, and PDX1 p.E69A mutations were identified for the first time as causative variants for MODY. More aggressive clinical features were observed in three patients with double- and triple-heterozygosity of PDX1-KLF11 (p.E69A/p.S182R), CEL-ABCC8-KCNJ11 (p.I336, p.G157R/p.R1103Q/p.A157A), and HNF1B-KLF11 (p.S362P/p.P261L). Interestingly, the clinical effects of the BLK mutations appear to be exacerbated in the presence of obesity. In conclusion, NGS analyses of the adult patients with suspected MODY appear to be informative in a clinical context. These findings warrant further clinical diagnostic research and development in different world populations suffering from diabetes with genetic underpinnings.

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.

Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas.

Although many studies have been conducted on single gene therapies in cancer patients, the reality is that tumor arises from different coordinating protein groups. Unveiling perturbations in protein interactome related to the tumor formation may contribute to the development of effective diagnosis, treatment strategies, and prognosis. In this study, considering the clinical and transcriptome data of three Renal Cell Carcinoma (RCC) subtypes (ccRCC, pRCC, and chRCC) retrieved from The Cancer Genome Atlas (TCGA) and the human protein interactome, the differential protein-protein interactions were identified in each RCC subtype. The approach enabled the identification of differentially interacting proteins (DIPs) indicating prominent changes in their interaction patterns during tumor formation. Further, diagnostic and prognostic performances were generated by taking into account DIP clusters which are specific to the relevant subtypes. Furthermore, considering the mesenchymal epithelial transition (MET) receptor tyrosine kinase (PDB ID: 3DKF) as a potential drug target specific to pRCC, twenty-one lead compounds were identified through virtual screening of ZINC molecules. In this study, we presented remarkable findings in terms of early diagnosis, prognosis, and effective treatment strategies, that deserve further experimental and clinical efforts.

Current State of "Omics" Biomarkers in Pancreatic Cancer.

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

Cancer Stem Cell Transcriptome Profiling Reveals Seed Genes of Tumorigenesis: New Avenues for Cancer Precision Medicine.

Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 "seed genes" that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as "systems biomarkers" for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.

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.

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 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.

ETS-Domain Transcription Factor Elk-1 Regulates Stemness Genes in Brain Tumors and CD133+ BrainTumor-Initiating Cells.

Elk-1, a member of the ternary complex factors (TCFs) within the ETS (E26 transformation-specific) domain superfamily, is a transcription factor implicated in neuroprotection, neurodegeneration, and brain tumor proliferation. Except for known targets, c-fos and egr-1, few targets of Elk-1 have been identified. Interestingly, SMN, SOD1, and PSEN1 promoters were shown to be regulated by Elk-1. On the other hand, Elk-1 was shown to regulate the CD133 gene, which is highly expressed in brain-tumor-initiating cells (BTICs) and used as a marker for separating this cancer stem cell population. In this study, we have carried out microarray analysis in SH-SY5Y cells overexpressing Elk-1-VP16, which has revealed a large number of genes significantly regulated by Elk-1 that function in nervous system development, embryonic development, pluripotency, apoptosis, survival, and proliferation. Among these, we have shown that genes related to pluripotency, such as Sox2, Nanog, and Oct4, were indeed regulated by Elk-1, and in the context of brain tumors, we further showed that Elk-1 overexpression in CD133+ BTIC population results in the upregulation of these genes. When Elk-1 expression is silenced, the expression of these stemness genes is decreased. We propose that Elk-1 is a transcription factor upstream of these genes, regulating the self-renewal of CD133+ BTICs.

Monogenic Childhood Diabetes: Dissecting Clinical Heterogeneity by Next-Generation Sequencing in Maturity-Onset Diabetes of the Young.

Diabetes is a common disorder with a heterogeneous clinical presentation and an enormous burden on health care worldwide. About 1-6% of patients with diabetes suffer from maturity-onset diabetes of the young (MODY), the most common form of monogenic diabetes with autosomal dominant inheritance. MODY is genetically and clinically heterogeneous and caused by genetic variations in pancreatic ß-cell development and insulin secretion. We report here new findings from targeted next-generation sequencing (NGS) of 13 MODY-related genes. A sample of 22 unrelated pediatric patients with MODY and 13 unrelated healthy controls were recruited from a Turkish population. Targeted NGS was performed with Miseq 4000 (Illumina) to identify genetic variations in 13 MODY-related genes: HNF4A, GCK, HNF1A, PDX1, HNF1B, NEUROD1, KLF11, CEL, PAX4, INS, BLK, ABCC8, and KCNJ11. The NGS data were analyzed adhering to the Genome Analysis ToolKit (GATK) best practices pipeline, and variant filtering and annotation were performed. In the patient sample, we identified 43 MODY-specific genetic variations that were not present in the control group, including 11 missense mutations and 4 synonymous mutations. Importantly, and to the best of our knowledge, the missense mutations NEUROD1 p.D202E, KFL11 p.R461Q, BLK p.G248R, and KCNJ11 p.S385F were first associated with MODY in the present study. These findings contribute to the worldwide knowledge base on MODY and molecular correlates of clinical heterogeneity in monogenic childhood diabetes. Further comparative population genetics and functional genomics studies are called for, with an eye to discovery of novel diagnostics and personalized medicine in MODY. Because MODY is often misdiagnosed as type 1 or type 2 diabetes mellitus, advances in MODY diagnostics with NGS stand to benefit diabetes overall clinical care as well.

Transcriptomic profile of Pea3 family members reveal regulatory codes for axon outgrowth and neuronal connection specificity.

PEA3 transcription factor subfamily is present in a variety of tissues with branching morphogenesis, and play a particularly significant role in neural circuit formation and specificity. Many target genes in axon guidance and cell-cell adhesion pathways have been identified for Pea3 transcription factor (but not for Erm or Er81); however it was not so far clear whether all Pea3 subfamily members regulate same target genes, or whether there are unique targets for each subfamily member that help explain the exclusivity and specificity of these proteins in neuronal circuit formation. In this study, using transcriptomics and qPCR analyses in SH-SY5Y neuroblastoma cells, hypothalamic and hippocampal cell line, we have identified cell type-specific and subfamily member-specific targets for PEA3 transcription factor subfamily. While Pea3 upregulates transcription of Sema3D and represses Sema5B, for example, Erm and Er81 upregulate Sema5A and Er81 regulates Unc5C and Sema4G while repressing EFNB3 in SH-SY5Y neuroblastoma cells. We furthermore present a molecular model of how unique sites within the ETS domain of each family member can help recognize specific target motifs. Such cell-context and member-specific combinatorial expression profiles help identify cell-cell and cell-extracellular matrix communication networks and how they establish specific connections.

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.

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.

Cancer Drug Repositioning by Comparison of Gene Expression in Humans and Axolotl (Ambystoma mexicanum) During Wound Healing.

Urodele amphibians such as the axolotl (Ambystoma mexicanum) display a large capacity for tissue regeneration and remarkable resistance to cancer. As a model organism, axolotl thus offers a unique opportunity for cancer research and anticancer drug discovery, not to mention the discerning mechanisms that underpin controlled cellular growth and regeneration versus cancer. To the best of our knowledge, little is known on comparative gene expression changes during regeneration events such as wound healing in axolotl and humans. Using publicly available transcriptomics data and bioinformatics analyses, we examined the differential gene expression signatures in skin wound samples from axolotl and humans after skin biopsy punch injury, in comparison with intact (uninjured) control skin samples. We identified 95 genes exhibiting a reversal expression pattern between humans and axolotl during the wound healing/regeneration period. These genes were significantly associated with collagen biosynthesis, extracellular matrix organization, PI3K-Akt signaling pathway, immune system response, and apoptotic process. Furthermore, this new gene set exhibited high prognostic performance in discriminating the survival risk in skin-related cancers, including melanoma (hazard ratio [HR] = 8.14, p < 10-30), oral cancer (HR >100, p < 10-12), and head and neck carcinoma (HR = 5.29, p < 10-30). Moreover, considering these gene signatures, we repositioned 11 small molecules as potential anticancer drug candidates indicating reversal effects on upregulated human genes and downregulated axolotl genes or mimicking downregulated human genes and upregulated axolotl genes. We anticipate that this study offers new insights on gene signatures bridging regeneration mechanisms with tumorigenesis and cancer drug repositioning.