Synthesis and cytotoxicity of novel 6,8,9-trisubstituted purine analogs against liver cancer cells.

A series of novel 6-(substituted phenyl piperazine)-8-(4-substituted phenyl)-9-cyclopentyl purines, 10-51, were synthesized by a four-step synthesis, achieving an overall yield of about 43 %. The reaction conditions were effectively optimized, and the final products were obtained with high purity and yield in all synthesis steps. The synthesized nucleobases were evaluated for their in vitro cytotoxic activities on selected human cancer cell lines (HUH7 (liver), HCT116 (colon), and MCF7 (breast)) using the Sulforhodamine B (SRB) assay. Among these analogs, compounds bearing 4-trifluoromethyl phenyl (19, 20 and 21), 4-methoxy phenyl (27) and 4-fluoro phenyl (34) substitutions at C-8 of purine were the most potent, and they were also analyzed in drug-resistance and drug-sensitive hepatocellular cancer cell (HCC) panels. Compound 19 displayed remarkable anticancer activities (IC50 = 2.9-9.3 µM) against Huh7, FOCUS, SNU475, SNU182, HepG2, and Hep3B cells compared to the positive control, Fludarabine. Additionally, the pharmacological properties and toxicity profiles of the molecules were investigated computationally by the Swiss-ADME and Pro-Tox II online tools, respectively. Results showed that our compounds have favorable physicochemical characteristics for oral bioavailability and do not reveal any toxicity endpoints such as carcinogenicity, immunotoxicity, mutagenicity, or cytotoxicity.

Exploration of novel 6,8,9-trisubstituted purine analogues: synthesis, in vitro biological evaluation, and their effect on human cancer cells.

Cancer, a leading global cause of mortality, demands continuous advancements in therapeutic strategies. This study focuses on the design and synthesis of a novel series of purine derivatives, specifically 6-(substituted phenyl piperazine)-8-(4-phenoxyphenyl)-9-cyclopentyl purine derivatives (5-11). The motivation behind this endeavor lies in addressing acquired resistance mechanisms in cancer cells, a significant hurdle in current treatment modalities. The synthesis, starting from 4,6-dichloro-5-nitropyrimidine, involves a multi-step process, resulting in seven new purine derivatives. Biological evaluation against human liver, colon, and breast cancer cells (Huh7, HCT116, and MCF7, respectively) was performed using the SRB assay. Among the synthesized analogs, compounds 5 and 6, exhibited notable cytotoxic activity, surpassing clinically used positive controls 5-Fluorouracil and Fludarabine in terms of efficacy. This research underscores the potential of purine derivatives with a phenyl group at the C-8 position as a scaffold for developing compounds with improved anticancer properties. The findings offer insights for future exploration and development of novel agents in cancer pharmaceutical research.

An integrative framework for clinical diagnosis and knowledge discovery from exome sequencing data.

Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel variations restrict their use in clinical applications. We propose the Pathogenic Mutation Prediction (PMPred) method to predict the pathogenicity of single nucleotide variations, which impair protein function by prematurely terminating a protein's elongation during its synthesis. The prediction starts by monitoring functional effects (Gene Ontology annotation changes) of the change in sequence, using an existing ensemble machine learning model (UniGOPred). This, in turn, reveals the mutations that significantly deviate functionally from the wild-type sequence. We have identified novel harmful mutations in patient data and present them as motivating case studies. We also show that our method has increased sensitivity and specificity compared to state-of-the-art, especially in single nucleotide variations that produce large functional changes in the final protein. As further validation, we have done a comparative docking study on such a variation that is misclassified by existing methods and, using the altered binding affinities, show how PMPred can correctly predict the pathogenicity when other tools miss it. PMPred is freely accessible as a web service at https://pmpred.kansil.org/, and the related code is available at https://github.com/kansil/PMPred.

Newly synthesized 6-substituted piperazine/phenyl-9-cyclopentyl containing purine nucleobase analogs act as potent anticancer agents and induce apoptosis via inhibiting Src in hepatocellular carcinoma cells.

Newly synthesized 6-substituted piperazine/phenyl-9-cyclopentyl-containing purine nucleobase analogs were tested for their in vitro anticancer activity against human cancer cells. Compounds 15, 17-24, 49, and 56 with IC50 values less than 10 µM were selected for further examination on an enlarged panel of liver cancer cell lines. Experiments revealed that compound 19 utilizes its high cytotoxic potential (IC50 < 5 µM) to induce apoptosis in vitro. Compound 19 displayed a KINOMEscan selectivity score S35 of 0.02 and S10 of 0.01 and demonstrated a significant selectivity against anaplastic lymphoma kinase (ALK) and Bruton's tyrosine kinase (BTK) over other kinases. Compounds 19, 21, 22, 23, and 56 complexed with ALK, BTK, and (discoidin domain-containing receptor 2) DDR2 were analyzed structurally for binding site interactions and binding affinities via molecular docking and molecular dynamics simulations. Compounds 19 and 56 displayed similar interactions with the activation loop of the kinases, while only compound 19 reached toward the multiple subsites of the active site. Cell cycle and signaling pathway analyses exhibited that compound 19 decreases phosho-Src, phospho-Rb, cyclin E, and cdk2 levels in liver cancer cells, eventually inducing apoptosis.

Mitochondrial One-Carbon Metabolism is Required for TGF-ß-Induced Glycine Synthesis and Collagen Protein Production.

A hallmark of Idiopathic Pulmonary Fibrosis is the TGF-ß-dependent activation of lung fibroblasts, leading to excessive deposition of collagen proteins and progressive scarring. We have previously shown that synthesis of collagen by lung fibroblasts requires de novo synthesis of glycine, the most abundant amino acid in collagen protein. TGF-ß upregulates the expression of the enzymes of the de novo serine/glycine synthesis pathway in lung fibroblasts through mTORC1 and ATF4-dependent transcriptional programs. SHMT2, the final enzyme of the de novo serine/glycine synthesis pathway, transfers a one-carbon unit from serine to tetrahydrofolate (THF), producing glycine and 5,10-methylene-THF (meTHF). meTHF is converted back to THF in the mitochondrial one-carbon (1C) pathway through the sequential actions of MTHFD2 (which converts meTHF to 10-formyl-THF), and either MTHFD1L, which produces formate, or ALDH1L2, which produces CO2. It is unknown how the mitochondrial 1C pathway contributes to glycine biosynthesis or collagen protein production in fibroblasts, or fibrosis in vivo. Here, we demonstrate that TGF-ß induces the expression of MTHFD2, MTHFD1L, and ALDH1L2 in human lung fibroblasts. MTHFD2 expression was required for TGF-ß-induced cellular glycine accumulation and collagen protein production. Combined knockdown of both MTHFD1L and ALDH1L2 also inhibited glycine accumulation and collagen protein production downstream of TGF-ß; however knockdown of either protein alone had no inhibitory effect, suggesting that lung fibroblasts can utilize either enzyme to regenerate THF. Pharmacologic inhibition of MTHFD2 recapitulated the effects of MTHFD2 knockdown in lung fibroblasts and ameliorated fibrotic responses after intratracheal bleomycin instillation in vivo. Our results provide insight into the metabolic requirements of lung fibroblasts and provide support for continued development of MTHFD2 inhibitors for the treatment of IPF and other fibrotic diseases.

Loss of heme oxygenase 2 causes reduced expression of genes in cardiac muscle development and contractility and leads to cardiomyopathy in mice.

Obstructive sleep apnea (OSA) is a common breathing disorder that affects a significant portion of the adult population. In addition to causing excessive daytime sleepiness and neurocognitive effects, OSA is an independent risk factor for cardiovascular disease; however, the underlying mechanisms are not completely understood. Using exposure to intermittent hypoxia (IH) to mimic OSA, we have recently reported that mice exposed to IH exhibit endothelial cell (EC) activation, which is an early process preceding the development of cardiovascular disease. Although widely used, IH models have several limitations such as the severity of hypoxia, which does not occur in most patients with OSA. Recent studies reported that mice with deletion of hemeoxygenase 2 (Hmox2-/-), which plays a key role in oxygen sensing in the carotid body, exhibit spontaneous apneas during sleep and elevated levels of catecholamines. Here, using RNA-sequencing we investigated the transcriptomic changes in aortic ECs and heart tissue to understand the changes that occur in Hmox2-/- mice. In addition, we evaluated cardiac structure, function, and electrical properties by using echocardiogram and electrocardiogram in these mice. We found that Hmox2-/- mice exhibited aortic EC activation. Transcriptomic analysis in aortic ECs showed differentially expressed genes enriched in blood coagulation, cell adhesion, cellular respiration and cardiac muscle development and contraction. Similarly, transcriptomic analysis in heart tissue showed a differentially expressed gene set enriched in mitochondrial translation, oxidative phosphorylation and cardiac muscle development. Analysis of transcriptomic data from aortic ECs and heart tissue showed loss of Hmox2 gene might have common cellular network footprints on aortic endothelial cells and heart tissue. Echocardiographic evaluation showed that Hmox2-/- mice develop progressive dilated cardiomyopathy and conduction abnormalities compared to Hmox2+/+ mice. In conclusion, we found that Hmox2-/- mice, which spontaneously develop apneas exhibit EC activation and transcriptomic and functional changes consistent with heart failure.

Transfer learning for drug-target interaction prediction.

MOTIVATION: Utilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach. RESULTS: Here, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org.

Novel Indole-Pyrazole Hybrids as Potential Tubulin-Targeting Agents; Synthesis, antiproliferative evaluation, and molecular modeling studies.

Structurally diverse indole-3-pyrazole-5-carboxamide analogues (10-29) were designed, synthesized, and evaluated for their antiproliferative activity against three cancer cell lines (Huh7, MCF-7, and HCT116) using the sulforhodamine B assay. Some of the derivatives showed anticancer activities equal to or better than sorafenib against cancer cell lines. Compounds 18 showed potent activity against the hepatocellular cancer (HCC) cell lines, with IC50 values in the range 0.6-2.9 µM. Compound 18 also exhibited moderate inhibitory activity against tubulin polymerization (IC50 = 19 µM). Flow cytometric analysis of cultured cells treated with 18 also demonstrated that the compound caused cell cycle arrest at the G2/M phase in both Huh7 and Mahlavu cells and induced apoptotic cell death in HCC cells. Docking simulations were performed to determine possible modes of interaction between 18 and the colchicine site of tubulin and quantum mechanical calculations were performed to observe the electronic nature of 18 and to support docking results.

Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function.

This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial features within the hyperspectral cube are incorporated to train a strong classifier. Unlike 2D convolutions, 3D convolutions take the spectral dimension into account while automatically collecting distinctive features during the CNN training stage. As a result, we have avoided manual feature engineering on hyperspectral data and proposed a compact method for HSI medical applications. Moreover, the focal loss function, utilized as a CNN cost function, enables our model to tackle the class imbalance problem residing in the dataset effectively. The focal loss function emphasizes the hard examples to learn and prevents overfitting due to the lack of inter-class balancing. Our empirical results demonstrate the superiority of hyperspectral data over RGB data for liver cancer tissue classification. We have observed that increased spectral dimension results in higher classification accuracy. Both spectral and spatial features are essential in training an accurate learner for cancer tissue classification.

Intermittent hypoxia inhibits epinephrine-induced transcriptional changes in human aortic endothelial cells.

Obstructive sleep apnea (OSA) is an independent risk factor for cardiovascular disease. While intermittent hypoxia (IH) and catecholamine release play an important role in this increased risk, the mechanisms are incompletely understood. We have recently reported that IH causes endothelial cell (EC) activation, an early phenomenon in the development of cardiovascular disease, via IH-induced catecholamine release. Here, we investigated the effects of IH and epinephrine on gene expression in human aortic ECs using RNA-sequencing. We found a significant overlap between IH and epinephrine-induced differentially expressed genes (DEGs) including enrichment in leukocyte migration, cytokine-cytokine receptor interaction, cell adhesion and angiogenesis. Epinephrine caused higher number of DEGs compared to IH. Interestingly, IH when combined with epinephrine had an inhibitory effect on epinephrine-induced gene expression. Combination of IH and epinephrine induced MT1G (Metallothionein 1G), which has been shown to be highly expressed in ECs from parts of aorta (i.e., aortic arch) where atherosclerosis is more likely to occur. In conclusion, epinephrine has a greater effect than IH on EC gene expression in terms of number of genes and their expression level. IH inhibited the epinephrine-induced transcriptional response. Further investigation of the interaction between IH and epinephrine is needed to better understand how OSA causes cardiovascular disease.

Vicinal Diaryl-Substituted Isoxazole and Pyrazole Derivatives with In Vitro Growth Inhibitory and In Vivo Antitumor Activity.

The vicinal diaryl heterocyclic framework has been widely used for the development of compounds with significant bioactivities. In this study, a series of diaryl heterocycles were designed and synthesized based on an in-house diaryl isoxazole derivative (9), and most of the newly synthesized derivatives demonstrated moderate to good antiproliferative activities against a panel of hepatocellular carcinoma and breast cancer cells, exemplified with the diaryl isoxazole 11 and the diaryl pyrazole 85 with IC50 values in the range of 0.7-9.5 µM. Treatments with both 11 and 85 induced apoptosis in these tumor cells, and they displayed antitumor activity in vivo in the Mahlavu hepatocellular carcinoma and the MDA-MB-231 breast cancer xenograft models, indicating that these compounds could be considered as leads for further development of antitumor agents. Important structural features of this compound class for the antitumor activity have also been proposed, which warrant further exploration to guide the design of new and more potent diaryl heterocycles.

A new triazolothiadiazine derivative inhibits stemness and induces cell death in HCC by oxidative stress dependent JNK pathway activation.

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer, and resistant to both conventional and targeted chemotherapy. Recently, nonsteroidal anti-inflammatory drugs (NSAIDs) have been shown to decrease the incidence and mortality of different types of cancers. Here, we investigated the cellular bioactivities of a series of triazolothiadiazine derivatives on HCC, which have been previously reported as potent analgesic/anti-inflammatory compounds. From the initially tested 32 triazolothiadiazine NSAID derivatives, 3 compounds were selected based on their IC50 values for further molecular assays on 9 different HCC cell lines. 7b, which was the most potent compound, induced G2/M phase cell cycle arrest and apoptosis in HCC cells. Cell death was due to oxidative stress-induced JNK protein activation, which involved the dynamic involvement of ASK1, MKK7, and c-Jun proteins. Moreover, 7b treated nude mice had a significantly decreased tumor volume and prolonged disease-free survival. 7b also inhibited the migration of HCC cells and enrichment of liver cancer stem cells (LCSCs) alone or in combination with sorafenib. With its ability to act on proliferation, stemness and the migration of HCC cells, 7b can be considered for the therapeutics of HCC, which has an increased incidence rate of ~ 3% annually.

SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins.

SUMMARY: Accurate prediction of the subcellular locations (SLs) of proteins is a critical topic in protein science. In this study, we present SLPred, an ensemble-based multi-view and multi-label protein subcellular localization prediction tool. For a query protein sequence, SLPred provides predictions for nine main SLs using independent machine-learning models trained for each location. We used UniProtKB/Swiss-Prot human protein entries and their curated SL annotations as our source data. We connected all disjoint terms in the UniProt SL hierarchy based on the corresponding term relationships in the cellular component category of Gene Ontology and constructed a training dataset that is both reliable and large scale using the re-organized hierarchy. We tested SLPred on multiple benchmarking datasets including our-in house sets and compared its performance against six state-of-the-art methods. Results indicated that SLPred outperforms other tools in the majority of cases. AVAILABILITY AND IMPLEMENTATION: SLPred is available both as an open-access and user-friendly web-server (https://slpred.kansil.org) and a stand-alone tool (https://github.com/kansil/SLPred). All datasets used in this study are also available at https://slpred.kansil.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HIF-1α induces glycolytic reprograming in tissue-resident alveolar macrophages to promote cell survival during acute lung injury.

Cellular metabolism is a critical regulator of macrophage effector function. Tissue-resident alveolar macrophages (TR-AMs) inhabit a unique niche marked by high oxygen and low glucose. We have recently shown that in contrast to bone marrow-derived macrophages (BMDMs), TR-AMs do not utilize glycolysis and instead predominantly rely on mitochondrial function for their effector response. It is not known how changes in local oxygen concentration that occur during conditions such as acute respiratory distress syndrome (ARDS) might affect TR-AM metabolism and function; however, ARDS is associated with progressive loss of TR-AMs, which correlates with the severity of disease and mortality. Here, we demonstrate that hypoxia robustly stabilizes HIF-1α in TR-AMs to promote a glycolytic phenotype. Hypoxia altered TR-AM metabolite signatures, cytokine production, and decreased their sensitivity to the inhibition of mitochondrial function. By contrast, hypoxia had minimal effects on BMDM metabolism. The effects of hypoxia on TR-AMs were mimicked by FG-4592, a HIF-1α stabilizer. Treatment with FG-4592 decreased TR-AM death and attenuated acute lung injury in mice. These findings reveal the importance of microenvironment in determining macrophage metabolic phenotype and highlight the therapeutic potential in targeting cellular metabolism to improve outcomes in diseases characterized by acute inflammation.

Context dependent isoform specific PI3K inhibition confers drug resistance in hepatocellular carcinoma cells.

BACKGROUND: Targeted therapies for Primary liver cancer (HCC) is limited to the multi-kinase inhibitors, and not fully effective due to the resistance to these agents because of the heterogeneous molecular nature of HCC developed during chronic liver disease stages and cirrhosis. Although combinatorial therapy can increase the efficiency of targeted therapies through synergistic activities, isoform specific effects of the inhibitors are usually ignored. This study concentrated on PI3K/Akt/mTOR pathway and the differential combinatory bioactivities of isoform specific PI3K-α inhibitor (PIK-75) or PI3K-ß inhibitor (TGX-221) with Sorafenib dependent on PTEN context. METHODS: The bioactivities of inhibitors on PTEN adequate Huh7 and deficient Mahlavu cells were investigated with real time cell growth, cell cycle and cell migration assays. Differentially expressed genes from RNA-Seq were identified by edgeR tool. Systems level network analysis of treatment specific pathways were performed with Prize Collecting Steiner Tree (PCST) on human interactome and enriched networks were visualized with Cytoscape platform. RESULTS: Our data from combinatory treatment of Sorafenib and PIK-75 and TGX-221 showed opposite effects; while PIK-75 displays synergistic effects on Huh7 cells leading to apoptotic cell death, Sorafenib with TGX-221 display antagonistic effects and significantly promotes cell growth in PTEN deficient Mahlavu cells. Signaling pathways were reconstructed and analyzed in-depth from RNA-Seq data to understand mechanism of differential synergistic or antagonistic effects of PI3K-α (PIK-75) and PI3K-ß (TGX-221) inhibitors with Sorafenib. PCST allowed as to identify AOX1 and AGER as targets in PI3K/Akt/mTOR pathway for this combinatory effect. The siRNA knockdown of AOX1 and AGER significantly reduced cell proliferation in HCC cells. CONCLUSIONS: Simultaneously constructed and analyzed differentially expressed cellular networks presented in this study, revealed distinct consequences of isoform specific PI3K inhibition in PTEN adequate and deficient liver cancer cells. We demonstrated the importance of context dependent and isoform specific PI3K/Akt/mTOR signaling inhibition in drug resistance during combination therapies. ( https://github.com/cansyl/Isoform-spesific-PI3K-inhibitor-analysis ).

EGF-SNX3-EGFR axis drives tumor progression and metastasis in triple-negative breast cancers.

Epidermal growth factor receptor (EGFR) has critical roles in epithelial cell physiology. Over-expression and over-activation of EGFR have been implicated in diverse cancers, including triple-negative breast cancers (TNBCs), prompting anti-EGFR therapies. Therefore, developing potent therapies and addressing the inevitable drug resistance mechanisms necessitates deciphering of EGFR related networks. Here, we describe Sorting Nexin 3 (SNX3), a member of the recycling retromer complex, as a critical player in the epidermal growth factor (EGF) stimulated EGFR network in TNBCs. We show that SNX3 is an immediate and sustained target of EGF stimulation initially at the protein level and later at the transcriptional level, causing increased SNX3 abundance. Using a proximity labeling approach, we observed increased interaction of SNX3 and EGFR upon EGF stimulation. We also detected colocalization of SNX3 with early endosomes and endocytosed EGF. Moreover, we show that EGFR protein levels are sensitive to SNX3 loss. Transient RNAi models of SNX3 downregulation have a temporary reduction in EGFR levels. In contrast, long-term silencing forces cells to recover and overexpress EGFR mRNA and protein, resulting in increased proliferation, colony formation, migration, invasion in TNBC cells, and increased tumor growth and metastasis in syngeneic models. Consistent with these results, low SNX3 and high EGFR mRNA levels correlate with poor relapse-free survival in breast cancer patients. Overall, our results suggest that SNX3 is a critical player in the EGFR network in TNBCs with implications for other cancers dependent on EGFR activity.

Design, Synthesis and Biological Evaluation of Novel Triazolothiadiazoles Derived from NSAIDs as Anticancer Agents.

BACKGROUND: Although transplantation, surgical resection, and tumor ablation are treatment options available following early diagnosis of HCC, poor prognosis and high recurrence rates restrict the efficacy of these approaches. Hence, small molecules with high selectivity and bioactivity are urgently required. OBJECTIVE: This study presents the synthesis of a series of new triazolothiadiazole derivatives (1a-3j) with NSAID moieties and their cytotoxic bioactivities. METHODS: The new synthetic derivatives (1-3; 1a-3j) and NSAIDs ibuprofen, naproxen, and flurbiprofen that commonly used in clinics were screened against human liver (Huh7), breast (MCF7), and colon (HCT116) carcinoma cell lines under in vitro conditions via NCI-sulforhodamine B assay. RESULTS: The 4-methoxyphenyl substituted condensed derivatives 1h, 2h, and 3h were the most active compounds. Based on its high potency, compound 3h was selected for the further biological evaluation of hepatocellular carcinoma cell lines, and the mechanisms underlying cell death induced by 3h were determined. The results revealed that compound 3h induced apoptosis and cell cycle arrest in the sub G1 phase in human liver cancer cells. CONCLUSION: These new small molecules may be used for the development of new lead compounds.

Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.

PURPOSE: Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine. METHODS: In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system. RESULTS: Following the system-level evaluation and selection of critical genes/proteins and pathways to target, our deep learning-based drug/compound-target protein interaction predictors DEEPScreen and MDeePred have been employed for predicting new bioactive drugs and compounds for these critical targets. Finally, we embedded ligands of selected HCC-associated proteins which had a significant enrichment with the CROssBAR system into a 2-D space to identify and repurpose small molecule inhibitors as potential drug candidates based on their molecular similarities to known HCC drugs. CONCLUSIONS: We expect that these series of data-driven analyses can be used as a roadmap to propose early-stage potential inhibitors (from database-scale sets of compounds) to both HCC and other complex diseases, which may subsequently be analyzed with more targeted in silico and experimental approaches.

Synthesis of novel indole-isoxazole hybrids and evaluation of their cytotoxic activities on hepatocellular carcinoma cell lines.

BACKGROUND: Liver cancer is predicted to be the sixth most diagnosed cancer globally and fourth leading cause of cancer deaths. In this study, a series of indole-3-isoxazole-5-carboxamide derivatives were designed, synthesized, and evaluated for their anticancer activities. The chemical structures of these of final compounds and intermediates were characterized by using IR, HRMS, 1H-NMR and 13C-NMR spectroscopy and element analysis. RESULTS: The cytotoxic activity was performed against Huh7, MCF7 and HCT116 cancer cell lines using sulforhodamine B assay. Some compounds showed potent anticancer activities and three of them were chosen for further evaluation on liver cancer cell lines based on SRB assay and real-time cell growth tracking analysis. Compounds were shown to cause arrest in the G0/G1 phase in Huh7 cells and caused a significant decrease in CDK4 levels. A good correlation was obtained between the theoretical predictions of bioavailability using Molinspiration calculation, Lipinski's rule of five, and experimental verification. These investigations reveal that indole-isoxazole hybrid system have the potential for the development of novel anticancer agents. CONCLUSIONS: This study has provided data that will form the basis of further studies that aim to optimize both the design and synthesis of novel compounds that have higher anticancer activities.

Identification of an mRNA isoform switch for HNRNPA1 in breast cancers.

Roles of HNRNPA1 are beginning to emerge in cancers; however, mechanisms causing deregulation of HNRNPA1 function remain elusive. Here, we describe an isoform switch between the 3'-UTR isoforms of HNRNPA1 in breast cancers. We show that the dominantly expressed isoform in mammary tissue has a short half-life. In breast cancers, this isoform is downregulated in favor of a stable isoform. The stable isoform is expressed more in breast cancers, and more HNRNPA1 protein is synthesized from this isoform. High HNRNPA1 protein levels correlate with poor survival in patients. In support of this, silencing of HNRNPA1 causes a reversal in neoplastic phenotypes, including proliferation, clonogenic potential, migration, and invasion. In addition, silencing of HNRNPA1 results in the downregulation of microRNAs that map to intragenic regions. Among these miRNAs, miR-21 is known for its transcriptional upregulation in breast and numerous other cancers. Altogether, the cancer-specific isoform switch we describe here for HNRNPA1 emphasizes the need to study gene expression at the isoform level in cancers to identify novel cases of oncogene activation.