A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations

Anticancer Effect of PtIIPHENSS, PtII5MESS, PtII56MESS and Their Platinum(IV)-Dihydroxy Derivatives against Triple-Negative Breast Cancer and Cisplatin-Resistant Colorectal Cancer

Development of resistance to cisplatin, oxaliplatin and carboplatin remains a challenge for their use as chemotherapies, particularly in breast and colorectal cancer. Here, we compare the anticancer effect of novel complexes [Pt(1,10-phenanthroline)(1S,2S-diaminocyclohexane)](NO3)2 (PtIIPHENSS), [Pt(5-methyl-1,10-phenanthroline)(1S,2S-diaminocyclohexane)](NO3)2 (PtII5MESS) and [Pt(5,6-dimethyl-1,10-phenanthroline)(1S,2S-diaminocyclohexane)](NO3)2 (PtII56MESS) and their platinum(IV)-dihydroxy derivatives with cisplatin. Complexes are greater than 11-fold more potent than cisplatin in both 2D and 3D cell line cultures with increased selectivity for cancer cells over genetically stable cells. ICP-MS studies showed cellular uptake occurred through an active transport mechanism with considerably altered platinum concentrations found in the cytoskeleton across all complexes after 24 h. Significant reactive oxygen species generation was observed, with reduced mitochondrial membrane potential at 72 h of treatment. Late apoptosis/necrosis was shown by Annexin V-FITC/PI flow cytometry assay, accompanied by increased sub-G0/G1 cells compared with untreated cells. An increase in S and G2+M cells was seen with all complexes. Treatment resulted in significant changes in actin and tubulin staining. Intrinsic and extrinsic apoptosis markers, MAPK/ERK and PI3K/AKT activation markers, together with autophagy markers showed significant activation of these pathways by Western blot. The proteomic profile investigated post-72 h of treatment identified 1597 MDA-MB-231 and 1859 HT29 proteins quantified by mass spectroscopy, with several differentially expressed proteins relative to no treatment. GO enrichment analysis revealed a statistically significant enrichment of RNA/DNA-associated proteins in both the cell lines and specific additional processes for individual drugs. This study shows that these novel agents function as multi-mechanistic chemotherapeutics, offering promising anticancer potential, and thereby supporting further research into their application as cancer therapeutics.

Structural Dynamics of P-Rex1 Complexed with Natural Leads Establishes the Protein as an Attractive Target for Therapeutics to Suppress Cancer Metastasis

Phosphatidylinositol 3,4,5-trisphosphate- (PIP3-) dependent Rac exchanger 1 (P-Rex1) functions as Rho guanine nucleotide exchange factor and is activated by synergistic activity of Gβγ and PIP3 of the heterotrimeric G protein. P-Rex1 activates Rac GTPases for regulating cell invasion and migration and promotes metastasis in several human cancers including breast, prostate, and skin cancer. The protein is a promising therapeutic target because of its multifunction roles in human cancers. Herein, the present study attempts to identify selective P-Rex1 natural inhibitors by targeting PIP3-binding pocket using large-size multiple natural molecule libraries. Each library was filtered subsequently in FAF-Drugs4 based on Lipinski's rule of five (RO5), toxicity, and filter pan assay interference compounds (PAINS). The output hits were virtually screened at the PIP3-binding pocket through PyRx AutoDock Vina and cross-checked by GOLD. The best binders at the PIP3-binding pocket were prioritized using a comparative analysis of the docking scores. Top-ranked two compounds with high GOLD fitness score (>80) and lowest AutoDock binding energy (< -12.7 kcal/mol) were complexed and deciphered for molecular dynamics along with control-P-Rex1 complex to validate compound binding conformation and disclosed binding interaction pattern. Both the systems were seen in good equilibrium, and along the simulation time, the compounds are in strong contact with the P-Rex1 PIP3-binding site. Hydrogen bonding analysis towards simulation end identified the formation of 16 and 22 short- and long-distance hydrogen bonds with different percent of occupancy to the PIP3 residues for compound I and compound 2, respectively. Radial distribution function (RDF) analysis of the key hydrogen bonds between the compound and the PIP3 residues demonstrated a strong affinity of the compounds to the mentioned PIP3 pocket. Additionally, MMGB/PBSA energies were performed that confirmed the dominance of Van der Waals energy in complex formation along with favorable contribution from hydrogen bonding. These findings were also cross-validated by a more robust WaterSwap binding energy predictor, and the results are in good agreement with a strong binding affinity of the compounds for the protein. Lastly, the key contribution of residues in interaction with the compounds was understood by binding free energy decomposition and alanine scanning methods. In short, the results of this study suggest that P-Rex1 is a good druggable target to suppress cancer metastasis; therefore, the screened druglike molecules of this study need in vitro and in vivo anti-P-Rex1 validation and may serve as potent leads to fight cancer.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Integrated computational approaches to screen gene expression data to determine key genes and therapeutic targets for type-2 diabetes mellitus

There is a rapid rise in cases of Type-2-diabetes mellitus (T2DM) globally, irrespective of the geography, ethnicity or any other variable factors. The molecular mechanisms that could cause the condition of T2DM need to be more thoroughly analysed to understand the clinical manifestations and to derive better therapeutic regimes. Tools in bioinformatics are used to trace out key gene elements and to identify the key causative gene elements and their possible therapeutic agents. Microarray datasets were retrieved from the Gene expression omnibus database and studied using R to derive different expressed gene (DEG) elements. With the comparison of the expressed genes with disease specific genes in DisGeNET, the final annotated genes were taken for analysis. Gene Ontology studies, Protein-protein interaction (PPI), Co-expression analysis, Gene-drug interactions were performed to scale down the hub genes and to identify the novelty across the genes analysed so far. In vivo and invitro analysis of key genes and the trace of interaction pathway is crucial to better understand the unique outcomes from the novel genes, forming the basis to understand the pathway that ends up causing T2DM. Afterwards, docking was executed enabling recognition of interacting residues involved in inhibition. The complex CCL5-265 and CD8A-40585 thus docked showed best results as is evident from its PCA analysis and MMGBSA calculation. There is now scope for deriving candidate drugs that could possibly detect personalized therapies for T2DM.

Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify 'high value' hits resulting from the CRISPR screen involves manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we build a recommendation system on top of a heterogeneous biomedical knowledge graph integrating pre-clinical, clinical, and literature evidence. The recommender system ranks genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identifies 57 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identifies previously unexplored resistance mechanisms that we prospectively validate.

Triple-Negative Breast Cancer: A Brief Review About Epidemiology, Risk Factors, Signaling Pathways, Treatment and Role of Artificial Intelligence

Triple-negative breast cancer (TNBC) is a kind of breast cancer that lacks estrogen, progesterone, and human epidermal growth factor receptor 2. This cancer is responsible for more than 15-20% of all breast cancers and is of particular research interest as it is therapeutically challenging mainly because of its low response to therapeutics and highly invasive nature. The non-availability of specific treatment options for TNBC is usually managed by conventional therapy, which often leads to relapse. The focus of this review is to provide up-to-date information related to TNBC epidemiology, risk factors, metastasis, different signaling pathways, and the pathways that can be blocked, immune suppressive cells of the TNBC microenvironment, current and investigation therapies, prognosis, and the role of artificial intelligence in TNBC diagnosis. The data presented in this paper may be helpful for researchers working in the field to obtain general and particular information to advance the understanding of TNBC and provide suitable disease management in the future.

A Comprehensive Computational Screening of Phytochemicals Derived from Saudi Medicinal Plants against Human CC Chemokine Receptor 7 to Identify Potential Anti-Cancer Therapeutics

Homeostatic trafficking of immune cells by CC chemokine receptor 7 (CCR7) keeps immune responses and tolerance in a balance. The involvement of this protein in lymph node metastasis in cancer marks CCR7 as a penitential drug target. Using the crystal structure of CCR7, herein, a comprehensive virtual screening study is presented to filter novel strong CCR7 binding phytochemicals from Saudi medicinal plants that have a higher binding affinity for the intracellular allosteric binding pocket. By doing so, three small natural molecules named as Hit-1 (1,8,10-trihydroxy-3-methoxy-6-methylanthracen-9(4H)-one), Hit-2 (4-(3,4-dimethoxybenzyl)-3-(4-hydroxy-3-methoxybenzyl)dihydrofuran-2(3H)-one), and Hit-3 (10-methyl-12,13-dihydro-[1,2]dioxolo[3,4,5-de]furo[3,2-g]isochromeno[4,3-b]chromen-8-ol) are predicted showing strong binding potential for the CC chemokine receptor 7 allosteric pocket. During molecular dynamics simulations, the compounds were observed in the formation of several chemical bonding of short bond distances. Additionally, the molecules remained in strong contact with the active pocket residues and experienced small conformation changes that seemed to be mediated by the CCR7 loops to properly engage the ligands. Two types of binding energy methods (MM/GBPBSA and WaterSwap) were additionally applied to further validate docking and simulation findings. Both analyses complement the good affinity of compounds for CCR7, the electrostatic and van der Waals energies being the most dominant in intermolecular interactions. The active pocket residue's role in compounds binding was further evaluated via alanine scanning, which highlighted their importance in natural compounds binding. Additionally, the compounds fulfilled all drug-like rules: Lipinski, Ghose, Veber, Egan, and Muegge passed many safety parameters, making them excellent anti-cancer candidates for experimental testing.

Kanser Teşhisinde Protein Haritalama Tekniklerinin Başarımlarının Derin Öğrenme Kullanılarak Karşılaştırılması

Kanser, dünya çapında çoğu insanın ölmesine neden olan ve birçok farklı alt tiplerden oluşan heterojen bir hastalıktır. Bir kanser türünün erken teşhisi ve prognozu, hastaların sonraki klinik takibini kolaylaştırabildiği için kanser araştırmalarında bir gereklilik haline gelmiştir. Bunun için en çok kullanılan yöntemlerden birisi histolojik incelemedir. Ancak bu yöntemde çok sayıda gözlemciler arası değişkenlik bulunmakta, bu ise inceleme sürecinin uzun olmasına ve zaman almasına neden olmaktadır. Bu dezavantajın önüne geçmek için araştırmacılar hesaplama-tabanlı yaklaşımlara yönelmişler ve kanserli proteinlerin belirlenmesi için protein-protein etkileşimleri, protein etkileşim ağları ve moleküler parmak izleri yöntemlerinden yararlanmaktadırlar. Bu yöntemler arasında, çeşitli çalışmalar genomik bilgilerden de kanserli hücrelerin tespit edilebildiğini göstermiştir. Kansere ait genlerin dizilimlerine göre belirli kanser türlerinin belirlenebildiği ve bu süreçte yapay öğrenme tabanlı yaklaşımların etkili olduğu görülmüştür. Bu çalışmada, derin öğrenme algoritmalarından birisi olan tekrarlayıcı sinir ağı mimarisi kullanılmış ve insana ait mesane, kolon ve prostat kanserlerinin, protein dizilimlerine göre sınıflandırılması yapılmıştır. Çalışma, verilerin elde edilmesi, protein dizilimlerinin sayısallaştırılması, derin öğrenme model uygulamasının geliştirilmesi ve protein haritalama tekniklerinin başarımının karşılaştırılması olmak üzere dört aşamadan meydana gelmektedir. Protein dizilimlerini sayısallaştırmak için AESNN1, hidrofobiklik, tam sayı, Miyazawa enerjileri ve rastgele kodlama yöntemleri ele alınmıştır. Çalışmanın sonunda, mesane kanseri için en yüksek doğruluk değeri %87.15 ile AESNN1 haritalama yöntemiyle, kolon kanseri ve prostat kanseri için ise en yüksek doğruluk değeri sırasıyla %94.40 ve %75.45 olarak Miyazawa enerjileri ve rastgele kodlama protein haritalama yöntemi ile elde edilmiştir. Bu çalışma ile yapay öğrenme ve protein haritalama tekniklerinin, kanserli protein dizilimlerinin belirlenmesinde etkili olduğu gözlemlenmiştir.

Structural insight into the binding pattern and interaction mechanism of chemotherapeutic agents with Sorcin by docking and molecular dynamic simulation

Sorcin (SOluble Resistance-related Calcium bInding proteiN) is a calcium binding protein that plays a key role in multidrug resistance (MDR) in human cancers. This study aimed at understanding the binding mechanism and structural basis for the interaction of structurally and functionally unrelated chemotherapeutic agent, namely doxorubicin, etoposide, omacetaxine mepesuccinate and paclitaxel with Sorcin by utilizing docking and molecular dynamic simulation approaches. The docking evaluation of etoposide, omacetaxine mepesuccinate and paclitaxel have shown a high affinity binding with Sorcin at the Ca²⁺-binding C-terminal domain (SCBD) in a comparable mode and affinity of binding to doxorubicin. Moreover, all of the docked compounds were shown to interact both hydrophilically and hydrophobically with the same residues within the active pocket which is located at interface of the Sorcin and collectively formed by EF5 loop, G helix and EF4 loop. However, the MD simulations revealed that the dynamics of Sorcin structure is different in the presence of the compounds when compared and contrasted to the Apo Sorcin, particularly in the first 25 ns, after which each system gained considerable structure stability. The difference in dynamics might be the outcome of high N and C-terminal flexibility that seem not to disturb compounds binding conformation but more likely is affecting chemical interaction network by breaking and establishing old and new hydrogen bonds, respectively. This detailed mechanistic understanding of different chemotherapeutic agents binding to Sorcin might be useful to open windows for designing and developing new inhibitors that are potentially capable of reversing the MDR in human cancers.

Mutational Landscape of Pirin and Elucidation of the Impact of Most Detrimental Missense Variants That Accelerate the Breast Cancer Pathways: A Computational Modelling Study

Pirin (PIR) protein is highly conserved in both prokaryotic and eukaryotic organisms. Recently, it has been identified that PIR positively regulates breast cancer cell proliferation, xenograft tumor formation, and metastasis, through an enforced transition of G1/S phase of the cell cycle by upregulation of E2F1 expression at the transcriptional level. Keeping in view the importance of PIR in many crucial cellular processes in humans, we used a variety of computational tools to identify non-synonymous single-nucleotide polymorphisms (SNPs) in the PIR gene that are highly deleterious for the structure and function of PIR protein. Out of 173 SNPs identified in the protein, 119 are non-synonymous, and by consensus, 24 mutations were confirmed to be deleterious in nature. Mutations such as V257A, I28T, and I264S were unveiled as highly destabilizing due to a significant stability fold change on the protein structure. This observation was further established through molecular dynamics (MD) simulation that demonstrated the role of the mutation in protein structure destability and affecting its internal dynamics. The findings of this study are believed to open doors to investigate the biological relevance of the mutations and drugability potential of the protein.

Quantitative proteomics characterization of cancer biomarkers and treatment

Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.