Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer.

Background: Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application. Objective: Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment. Design: As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach. Methods: We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs. Results: We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively. Conclusion: In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research.

Biomarkers, which are routinely used in clinics, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application. This research article highlights the challenges of translating cancer biomarkers into clinical practice and summarizes feasible step toward "in silico pre-clinical validation" using the example of lung cancer types. Accordingly, protein biomarkers proposed for lung cancer are being investigated using the "in silico pre-clinical validation" approach to determine whether they have cancer altering properties (i.e., oncoprotein, tumor suppressor, and cancer hallmark), are expressed in body fluids (i.e., plasma/serum, saliva, urine, and bronchoalveolar lavage) and can be targeted with FDA-approved drugs. We believe that the step of in silico pre-clinical validation is the future of biomarker research for all professionals involved in clinical, biological, epidemiological, biostatistical and health research, and that it will greatly facilitate the transition of biomarkers to the clinical phase.

Functions of Differentially Regulated miRNAs in Breast Cancer Progression: Potential Markers for Early Detection and Candidates for Therapy.

Breast cancer remains a major global health concern, emphasizing the need for reliable biomarkers to enhance early detection and therapeutic interventions. MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNA (~22 nt in length) molecules, which are aberrantly expressed in cancer and seem to influence tumor behavior and progression. Specific miRNA dysregulation has been associated with breast cancer initiation, proliferation, invasion, and metastasis. Understanding the functional roles of these miRNAs provides valuable insights into the intricate molecular mechanisms underlying breast cancer progression. The diagnostic potential of miRNAs as non-invasive biomarkers for early breast cancer detection is a burgeoning area of research. This review aims to elucidate the functions of differentially regulated miRNAs in breast cancer progression and assess their potential as markers for early detection, stage-specific biomarkers, and therapeutic targets. Furthermore, the ability of specific miRNAs to serve as prognostic indicators and predictors of treatment response highlights their potential clinical utility in guiding personalized therapeutic interventions.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Selective Impact of Selenium Compounds on Two Cytokine Storm Players.

COVID-19 patients suffer from the detrimental effects of cytokine storm and not much success has been achieved to overcome this issue. We sought to test the ability of selenium to reduce the impact of two important cytokine storm players: IL-6 and TNF-α. The effects of four selenium compounds on the secretion of these cytokines from THP-1 macrophages were evaluated in vitro following an LPS challenge. Also, the potential impact of methylseleninic acid (MSeA) on Nrf2 and IκBα was determined after a short treatment of THP-1 macrophages. MSeA was found to be the most potent selenium form among the four selenium compounds tested that reduced the levels of IL-6 and TNF-α secreted by THP-1 macrophages. In addition, an increase in Nrf2 and decrease in pIκBα in human macrophages was observed following MSeA treatment. Our data indicate that COVID-19 patients might benefit from the addition of MSeA to the standard therapy due to its ability to suppress the key players in the cytokine storm.

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.

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins.

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems' biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.

Evaluating electronic cigarette cytotoxicity and inflammatory responses in vitro.

INTRODUCTION: Cigarette smoking poses many health risks and can cause chronic obstructive pulmonary disease (COPD), cardiovascular disease, cancer of the lung and other organs. Smokers can substantially reduce their risks of these diseases by quitting, but nicotine addiction makes this difficult. Alternatives, such as electronic cigarettes (e-cigarettes), may provide a similar dose of nicotine, but expose users to fewer toxic chemicals than traditional cigarettes and may still be harmful especially for dual users, therefore, we sought to develop bioassays that can assess the potential toxicity and inflammatory response induced by e-cigarette liquids (e-liquids) with and without flavors. METHODS: E-liquids with varying nicotine content and flavors were aerosolized through growth media and exposed to human bronchial epithelial cell line (BEAS-2B) and human monocyte-macrophage cell line (THP-1) in vitro. Cytotoxicity in response to e-cigarette aerosols was measured by MTT assay in BEAS-2B cells and inflammatory response was measured by TNF-α, IL-6, IL-8, and MCP-1 released from THP-1 cells. In addition, the oxidative stress marker, REDD1, and impact on phagocytosis, was assessed following exposure of BEAS-2B and THP-1 derived macrophages, respectively. Cigarette smoke extract was used as a positive control with known cytotoxicity and impairment of inflammatory response. RESULTS: E-cigarette aerosols induced moderate cellular toxicity in bronchial epithelial cells. Our data also show that low nicotine levels are less damaging to the bronchial epithelial cells, and flavors in e-liquids influence the combined inflammatory response markers, phagocytosis, and REDD1 when examined in vitro. CONCLUSIONS: Our in vitro bioassays can be utilized to effectively measure flavor and nicotine-induced effects of e-cigarettes on combined inflammatory response and cytotoxicity in human macrophages and human bronchial epithelial cells, respectively.

Selenium Induces Pancreatic Cancer Cell Death Alone and in Combination with Gemcitabine.

Survival rate for pancreatic cancer remains poor and newer treatments are urgently required. Selenium, an essential trace element, offers protection against several cancer types and has not been explored much against pancreatic cancer specifically in combination with known chemotherapeutic agents. The present study was designed to investigate selenium and Gemcitabine at varying doses alone and in combination in established pancreatic cancer cell lines growing in 2D as well as 3D platforms. Comparison of multi-dimensional synergy of combinations' (MuSyc) model and highest single agent (HSA) model provided quantitative insights into how much better the combination performed than either compound tested alone in a 2D versus 3D growth of pancreatic cancer cell lines. The outcomes of the study further showed promise in combining selenium and Gemcitabine when evaluated for apoptosis, proliferation, and ENT1 protein expression, specifically in BxPC-3 pancreatic cancer cells in vitro.

Association of dietary sulfur amino acid intake with mortality from diabetes and other causes.

PURPOSE: Sulfur amino acid (SAA) consumption in Western countries is far greater than recommended levels. In preclinical studies, reduced SAA intake enhanced longevity and reduced risk for numerous chronic diseases. The current objective was to examine for associations between the intake of total SAA, including methionine (Met) and cysteine (Cys), and all-cause and disease-specific mortality US adults. METHODS: This prospective analysis included 15,083 US adult participants (mean age = 46.7 years) from the Third National Examination and Nutritional Health Survey (NHANES III, 1988-1994) with available mortality status (National Death Registry, 1988-2011). Dietary SAA intake was obtained from 24-h recall data. Associations between quintile (Q) of SAA intake (expressed as absolute intake or protein density) and mortality were assessed using Cox proportional hazard models and expressed as hazard ratio (HR). RESULTS: During follow-up (mean = 16.9 years), 4636 deaths occurred. After multivariable adjustment (including demographics and traditional risk factors, such as fat and other micronutrients intake), diabetes-caused mortality rates were nearly threefold higher in the highest compared to lowest SAA intake quintiles [HRQ5-Q1 total SAA, 2.68 (1.46-4.90); HRQ5-Q1 methionine, 2.45 (1.37-4.38); HRQ5-Q1 cysteine, 2.91 (1.57-5.37)] (P < 0.01)]. Higher total SAA protein density was also associated with diabetes-caused mortality [HRQ5-Q1 1.75 (1.31-2.35)]. Associations between SAA intake and all-cause mortality, and mortality caused by other major diseases were not detected. CONCLUSION: Results suggest that high-SAA diets are associated with increased risk for diabetes mortality and that lowering intake towards to Recommended Dietary Allowance levels could lead to reductions in lifetime risk.

Comparative proteomic analysis of exhaled breath condensate between lung adenocarcinoma and CT-detected benign pulmonary nodule patients.

BACKGROUND: Lung cancer is the leading cause of cancer mortality worldwide. The collection of exhaled breath condensate (EBC) is a non-invasive method that may have enormous potential as a biomarker for the early detection of lung cancer. OBJECTIVE: To investigate the proteomic differences of EBC between lung cancer and CT-detected benign nodule patients, and determine whether these proteins could be potential biomarkers. METHODS: Proteomic analysis was performed on individual samples from 10 lung cancer patients and 10 CT-detected benign nodule patients using data-independent acquisition (DIA) mass spectrometry. RESULTS: A total of 1,254 proteins were identified, and 21 proteins were differentially expressed in the lung adenocarcinoma group compared to the benign nodule group (p< 0.05). The GO analysis showed that most of these proteins were involved in neutrophil-related biological processes, and the KEGG analysis showed these proteins were mostly annotated to pyruvate and propanoate metabolism. Through protein-protein interactions (PPIs) analysis, ME1 and LDHB contributed most to the interaction-network of these proteins. CONCLUSION: Significantly differentially expressed proteins were detected between lung cancer and the CT-detected benign nodule group from EBC samples, and these proteins might serve as potential novel biomarkers of EBC for early lung cancer detection.

Changes in salivary proteome before and after cigarette smoking in smokers compared to sham smoking in nonsmokers: A pilot study.

INTRODUCTION: Smoking is the leading cause of preventable disease. Although smoking results in an acute effect of relaxation and positive mood through dopamine release, smoking is thought to increase stress symptoms such as heart rate and blood pressure from nicotine-induced effects on the HPA axis and increased cortisol. Despite the importance in understanding the mechanisms in smoking maintenance, little is known about the overall protein and physiological response to smoking. There may be multiple functions involved that if identified might help in improving methods for behavioral and pharmacological interventions. Therefore, our goal for this pilot study was to identify proteins in the saliva that change in response to an acute smoking event versus acute sham smoking event in smokers and non-smokers, respectively. METHODS: We employed the iTRAQ technique followed by Mass Spectrometry to identify differentially expressed proteins in saliva of smokers and non-smokers after smoking cigarettes and sham smoking, respectively. We also validated some of the salivary proteins by ELISA or western blotting. In addition, salivary cortisol and salivary amylase (sAA) activity were measured. RESULTS: In all, 484 salivary proteins were identified. Several proteins were elevated as well as decreased in smokers compared to non-smokers. Among these were proteins associated with stress response including fibrinogen alpha, cystatin A and sAA. Our investigation also highlights methodological considerations in study design, sampling and iTRAQ analysis. CONCLUSIONS: We suggest further investigation of other differentially expressed proteins in this study including ACBP, A2ML1, APOA4, BPIB1, BPIA2, CAH1, CAH6, CYTA, DSG1, EST1, GRP78, GSTO1, sAA, SAP, STAT, TCO1, and TGM3 that might assist in improving methods for behavioral and pharmacological interventions for smokers.

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.

Recent Developments in Cancer Systems Biology: Lessons Learned and Future Directions.

Cancer is a complex disease involving multiple mechanisms and critical players, at broad genomic, transcriptional, translational and/or biochemical levels [...].

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.

Proteomics of exhaled breath condensate in lung cancer and controls using data-independent acquisition (DIA): a pilot study.

Lung cancer, the leading cause of cancer mortality worldwide has a poor prognosis. To develop a non-invasive method for early lung cancer detection, exhaled breath condensate (EBC) was explored in this study. EBC samples were collected from lung cancer patients (n= 10) and healthy controls (n= 10), and a proteomic study was performed to identify potential biomarkers. Data-dependent acquisition was used to build the spectral library, and a data-independent acquisition (DIA) approach was applied for quantification of EBC proteomics. A total of 1151 proteins were identified, and several proteins were significantly upregulated in the lung cancer group compared to the control group. The Gene Ontology analysis revealed that most of the proteins were located within several organelles in the cells and were involved in binding and catalytic activity, and the Kyoto Encyclopedia Genes and Genomes results revealed that the proteins were mainly related to organismal systems and human disease. And S100A11, ANXA1, ENO1, and FABP5 might play a vital role in the EBC proteome. In summary, we demonstrated that the DIA-based quantification method was efficient in performing proteomic analysis in individual EBC samples, and some of the proteins might be novel biomarkers for lung cancer.

Association of sulfur amino acid consumption with cardiometabolic risk factors: Cross-sectional findings from NHANES III.

BACKGROUND: An average adult American consumes sulfur amino acids (SAA) at levels far above the Estimated Average Requirement (EAR) and recent preclinical data suggest that higher levels of SAA intake may be associated with a variety of aging-related chronic diseases. However, there are little data regarding the relationship between SAA intake and chronic disease risk in humans. The aim of this study was to examine the associations between consumption of SAA and risk factors for cardiometabolic diseases. METHODS: The sample included 11,576 adult participants of the Third National Examination and Nutritional Health Survey (NHANES III) Study (1988-1994). The primary outcome was cardiometabolic disease risk score (composite risk factor based on blood cholesterol, triglycerides, HDL, C-reactive protein (CRP), uric acid, glucose, blood urea nitrogen (BUN), glycated hemoglobin, insulin, and eGFR). Group differences in risk score by quintiles of energy-adjusted total SAA, methionine (Met), and cysteine (Cys) intake were determined by multiple linear regression after adjusting for age, sex, BMI, smoking, alcohol intake, and dietary factors. We further examined for associations between SAA intake and individual risk factors. FINDINGS: Mean SAA consumption was > 2.5-fold higher than the EAR. After multivariable adjustment, higher intake of SAA, Met, and Cys were associated with significant increases in composite cardiometabolic disease risk scores, independent of protein intake, and with several individual risk factors including serum cholesterol, glucose, uric acid, BUN, and insulin and glycated hemoglobin (p < 0.01). INTERPRETATION: Overall, our findings suggest that diets lower in SAA (close to the EAR) are associated with reduced risk for cardiometabolic diseases. Low SAA dietary patterns rely on plant-derived protein sources over meat derived foods. Given the high intake of SAA among most adults, our findings may have important public health implications for chronic disease prevention. FUNDING: This study does not have any funding.

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.

Methionine restriction delays aging-related urogenital diseases in male Fischer 344 rats.

Dietary methionine restriction (MR) has been found to enhance longevity across many species. We hypothesized that MR might enhance longevity in part by delaying or inhibiting age-related disease processes. To this end, male Fischer 344 rats were fed control (CF, 0.86% methionine) or MR (0.17% methionine) diets throughout their life until sacrifice at approximately 30 months of age, and histopathology was performed to identify the incidence and progression of two important aging-related pathologies, namely, chronic progressive nephropathy (CPN) and testicular tumorigenesis. Although kidney pathology was observed in 87% CF rats and CPN in 62% of CF animals, no evidence of kidney disease was observed in MR rats. Consistent with the absence of renal pathology, urinary albumin levels were lower in the MR group compared to controls throughout the study, with over a six-fold difference between the groups at 30 months of age. Biomarkers associated with renal disease, namely, clusterin, cystatin C, and ß-2 microglobulin, were reduced following 18 months of MR. A reduction in testicular tumor incidence from 88% in CF to 22% in MR rats was also observed. These results suggest that MR may lead to metabolic and cellular changes providing protection against age-related diseases.

Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer.

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.