Computational biology and chemistry最新文献

Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This study aims to compare and identify abnormal gene expression patterns in LUAD and LUSC samples relative to adjacent healthy tissues using an explainable artificial intelligence (XAI) framework. The LASSO algorithm was employed to identify the top gene features in the LUAD and LUSC datasets. An ensemble-based extreme gradient boosting (XGBoost) machine learning (ML) algorithm was trained and interpreted using SHapley Additive exPlanations (SHAP), with top features undergoing biological annotation through survival and functional enrichment analyses. The XAI-based SHAP module addresses the opaque nature of ML models. Notably, 35 and 33 genes were identified for LUAD and LUSC, respectively, using the LASSO algorithm. Performance metrics such as average accuracy and Matthew's correlation coefficient were evaluated. The XGBoost model demonstrated an average accuracy of 99.1 % for LUAD and 98.6 % for LUSC. The SFTPC gene emerged as the most significant feature across both NSCLC subtypes. For LUAD, genes such as STX11, CLEC3B, EMP2, and LYVE1 significantly influenced the XAI-SHAP framework. Conversely, GKN2, OGN, SLC39A8, and MMRN1 were identified for LUSC. Survival analysis and functional validation of these genes highlighted the physiological functions observed to be dysregulated in the NSCLC subtypes. These identified genes have the potential to enhance current medical diagnostics and therapeutics.

Background and objective: Prostate cancer (PCa) is the second most commonly diagnosed cancer in males, the mechanism of PCa with bone metastasis remains unclear. In this study, we aimed to utilize a retrospective clinical study to evaluate the diagnostic value of bone metastases from PCa and provide reference values for future applications.

Methods: We retrospectively collected a total of 200 samples including 100 PCa patients with bone metastatic and 100 without from June 2019 to August 2021. Transrectal ultrasonography (TRUS) was applied for observing the microvascular blood flow in the lesion. The serum levels of prostate specific antigen (PSA), vascular endothelial growth factor 2 (VEGF2), interleukin-6 (IL-6) and Pro-gastrin-releasing peptide (ProGRP) was determined using Enzyme-linked immunosorbent assay Kit. Regression model was constructed to analyze the risk factors for PCa with bone metastasis, the prognosis value of which was evaluated using receiver operating characteristic (ROC) curves. Ultimately, dataset GSE32269 was employed for validation.

Results: The focal blood perfusion was significantly improved in patients with bone metastasis than those without (P < 0.01). The examination results indicated that PCa patients with bone metastasis had higher levels of PSA, VEGF2, IL-6 and ProGRP than non-bone metastasis (P < 0.01). Moreover, the regression analysis indicated that the four cytokines were the risk factors for bone metastasis, and the ROC curves further confirmed that PSA and VEGF2 had high value of prediction value for bone metastasis with AUC of 0.901 and 0.8519.

Conclusion: The expression of PSA and VEGF2 in serum had high prognosis value for bone metastasis in PCa patients.

Due to the unclear selectivity of the protein system, designing selective small molecule inhibitors has been a significant challenge. This issue is particularly prominent in the phosphodiesterases (PDEs) system. Phosphodiesterase 1B (PDE1B) and phosphodiesterase 10 A (PDE10A) are two closely related subtypes of PDE proteins that play diverse roles in the immune system and tumorigenesis, respectively. Distinguishing the selective mechanism of these two subtypes is crucial for maximizing therapeutic efficacy and minimizing the side effects of inhibitors. We have investigated the interactions between crucial amino acid residues and selective inhibitors through several computer-aided drug design methods such as molecular docking, molecular dynamic simulation, MM/GBSA calculation, and alanine scanning mutagenesis revealing the selective inhibition mechanism between PDE1B and PDE10A. Our finding shows the selective residues of PDE1B are His373 and Gln421, while the selective residues for PDE10A are Tyr683 and Phe719. Specifically, PDE10A inhibitors form hydrogen bonds and hydrophobic interactions with Tyr683 and Phe719, whereas PDE1B inhibitors only demonstrate weak hydrophobic interactions in the corresponding region. Overall, elucidating the selectivity mechanism underlying the differential interaction between PDE1B and PDE10A is crucial for designing inhibitors with distinct selectivity towards PDE1B/10 A.

Background and objective: Castration-resistant prostate cancer (CRPC) is caused by resistance to androgen deprivation treatment and leads to the death of patients and there is almost no chance of survival. Therefore, finding a cure to overcome CRPC is challenging and important, but discovering a new drug is very time-consuming and expensive. To overcome these problems, we used Drug repositioning (drug repurposing) strategy in this study.

Methods: Gene expression data of CRPC and primary prostate samples were extracted from the GEO database to identify DEGs. Pathway enrichment was performed to find the role of DEGs in signaling pathways. To identify hub proteins, the PPI network was reconstructed and analyzed. drug candidates were identified and to select the most effective drug, molecular docking analysis, and molecular dynamics simulation were performed. Then MTT and qRT-PCR tests were performed to check the effectiveness of the selected drug.

Results: A total of 152 upregulated DEGs and 343 downregulated DEGs were identified, and after PPI network analysis, IKBKB, SNAP23, MYC, and NOTCH1 genes were introduced as hubs. drug candidates for IKBKB were identified and by examining the results of docking screening and molecular dynamics, sulfasalazine was selected as the most effective drug. Laboratory analyses proved the effectiveness of this drug and a decrease in the expression of all target genes was observed.

Conclusion: In this study, IKBKB key protein were identified in CRPC, and sulfasalazine was selected as a suitable candidate for drug repositioning and its effectiveness was confirmed through tests.

Background: Fatty acid metabolism (FAM) plays a critical role in tumor progression and therapeutic resistance by enhancing lipid biosynthesis, storage, and catabolism. Dysregulated FAM is a hallmark of prostate cancer (PCa), enabling cancer cells to adapt to extracellular signals and metabolic changes, with the tumor microenvironment (TME) playing a key role. However, the prognostic significance of FAM in PCa remains unexplored.

Methods: We analyzed 309 FAM-related genes to develop a prognostic model using least absolute shrinkage and selection operator (LASSO) regression based on The Cancer Genome Atlas (TCGA) database. This model stratified PCa patients into high- and low-risk groups and was validated using the Gene Expression Omnibus (GEO) database. We constructed a nomogram incorporating risk score, clinical variables (T and N stage, Gleason score, age), and assessed its performance with calibration curves. The associations between risk score, tumor mutation burden (TMB), immune checkpoint inhibitors (ICIs), and TME features were also examined. Finally, a hub gene was identified via protein-protein interaction (PPI) networks and validated.

Results: The risk score was an independent prognostic factor for PCa. High-risk patients showed worse survival outcomes but were more responsive to immunotherapy, chemotherapy, and targeted therapies. A core gene with high expression correlated with poor prognosis, unfavorable clinicopathological features, and immune cell infiltration.

Conclusion: These findings reveal the prognostic importance of FAM in PCa, providing novel insights into prognosis and potential therapeutic targets for PCa management.

Glycogen Synthase Kinase-3 beta (GSK-3β) is a crucial enzyme linked to various cellular processes, including neurodegeneration, autophagy, and diabetes. A structurally diverse set of 1293 molecules having GSK-3β modulatory activity has been used. Molecular docking and eXplainable Artificial Intelligence (XAI) have been used concomitantly. The approach involves using GA for feature selection and XGBoost for in-depth analysis, yielding strong statistical validation with R2tr = 0.9075, R2L10 %O = 0.9116, and Q2F3 = 0.7841. Molecular docking provided complementary and similar results. Machine learning model interpretation using SHapley Additive exPlanations (SHAP) revealed that specific structural features like aromatic carbon with specific partial charges, non-ring nitrogen atoms, sp³-hybrid carbon atoms, and the topological distance between carbon and nitrogen atoms, among others, significantly influence the modulatory profile. The results are also supported by reported X-ray resolved structures. In addition, in-silico ADMET analysis is also accomplished. This research underscores the value of advanced machine learning techniques in understanding complex biological phenomena and supporting rational drug design.

A series of substituted 2-(2-benzylidenehydrazinyl)benzothiazole Schiff-base derivatives and complexes containing Re(I) were synthesized and analyzed using various characterization techniques, including elemental analysis, conductance measurement, ¹H-NMR, FT-IR, and LC-MS. The biological activities of the compounds were evaluated. Binding affinity between the complexes and calf thymus DNA (CT-DNA) was conducted using UV-visible spectroscopy, viscosity measurement, fluorescence spectroscopy, and molecular docking studies, indicating intercalation binding mode. The broth dilution method evaluated antibacterial activity against two Gram-positive and three Gram-negative bacteria. The results demonstrated the effectiveness of each complex against the tested pathogens. The MTT assay examined cytotoxic qualities on MCF-7 cell lines, demonstrating strong cytotoxic effects. The lethality of brine prawn assay was employed to assess the toxicity of the compounds. The Schiff base was optimized using the 6-31 G (d, p) basis set and B3LYP techniques. Density functional theory calculations were performed to compare the bond angles and lengths of the synthesized compounds with experimental values, showing good agreement, and to calculate the related orbital energies. The therapeutic qualities were evaluated using an in silico ADMET model, which verified that the synthesized compounds have qualities similar to those of drugs.

Hearing impairment is a major global health problem, affecting more than 5 % of the world's population at various ages, from neonates to the elderly. Among the common genetic variations in humans, single nucleotide variations and small insertions or deletions predominate. The study of hearing loss resulting from these variations is proving invaluable in the analysis and diagnosis of hearing disorders. The identification of pathogenic mutations is frequently a lengthy and laborious process. Existing computational prediction tools have been developed primarily for common diseases and genome-wide analyses, with less focus on deafness. This study proposes a novel approach that focuses on the regions surrounding mutation sites. Mutation sites associated with deafness and their flanking regions of different lengths were extracted from relevant databases and combined into seven distinct segments of different lengths. The information-theoretic features of these segments were computed. Five machine learning algorithms were then used for training, resulting in the construction of a model capable of classifying and predicting deafness-related mutations. For fragments encompassing the 250 bp regions upstream and downstream of the mutations, the average AUC of the five classifiers on the independent test set is 0.89 and the average ACC is 0.85, indicating that the model has a high recognition rate of the pathogenic deafness mutation site. An ensemble approach was also applied to predict variants of uncertain significance (VUS) that may be associated with deafness. These variants were then scored and ranked to assess their likelihood of contributing to the condition.

Background: Centrosome amplification (CA) has been shown to be capable of initiating tumorigenesis with metastatic potential and enhancing cell invasion. We were interested in discovering how centrosome amplification-associated signature affects the prediction of prognosis and response to therapy in kidney renal clear cell carcinoma (KIRC).

Methods and materials: The TCGA-KIRC dataset was used to construct a centrosome amplification-associated signature using the random survival forest analysis and Cox regression analysis, and the ICGC and GEO datasets were employed for signature validation. Mutation and immune landscapes were outlined and the response to immunotherapy was evaluated. The expression of the screened hub gene was profiled by analyzing single-cell RNA sequencing from GSE159115.

Results: In the TCGA-KIRC cohort, 22 centrosome amplification-associated prognostic genes were discovered. According to the optimal consistency index (0.91), the random survival forest algorithm was selected to determine 7 hub prognostic genes, which were used to construct a centrosome amplification-associated prognostic index (CAAPI). It was discovered that it is connected to high mortality rates, high mutation rates, immunosuppressive cell infiltration, and immune dysfunction. For patients in the high CAAPI group, immunotherapy was not as effective. Single-cell RNA sequencing revealed a high expression of CDK5RAP3 in the tumor cells.

Conclusion: Centrosome amplification played a significant role in regulating tumor microenvironment and responding to immunotherapy, emphasizing its crucial importance in the development and treatment of KIRC. Patients with KIRC may benefit from using CAAPI as a biomarker to predict individual prognosis and assess a response to immunotherapy.

The aberrant metabolic reprogramming endows TNBC cells with sufficient ATP and lactate required for survival and metastasis. Hence, the intervention of the metabolic network represents a promising avenue to alleviate the Warburg effect in TNBC cells to impair their invasive and metastatic potential. Multitudinous in-silico analysis identified Enolase1 (ENO1) and the surface transporter protein, GLUT4 to be the potential targets for the abrogation of the metabolic network. The expression profiles of ENO1 and GLUT4 genes showed anomalous expression in various cancers, including breast cancer. Subsequently, the functional and physiological interactions of the target proteins were analyzed from the protein-protein interaction network. The pathway enrichment analysis identified the prime cancer signaling pathways in which these proteins are involved. Further, docking results bestowed Silibinin as the concurrent inhibitor of ENO1 and GLUT4. Moreover, the stable interaction of Silibinin with both proteins deciphered the binding free energies values of -48.86 and -104.31 KJ/mol from MMPBSA analysis and MD simulation, respectively. Furthermore, the cell viability, ROS assay, and live-dead imaging underscored the pronounced cytotoxicity of Silibinin, illuminating its capacity to incur apoptosis within TNBC cells. Additionally, glycolysis assay and gene expression analysis demonstrated the silibinin-mediated inhibition of the glycolysis pathway. Eventually, a lipidomic reprogramming towards fatty acid metabolism was established from the elevated lipid droplet accumulation, exogenous fatty acid uptake and de-novo lipogenesis. Nevertheless, repression of EMT and Wnt pathway progression by Silibinin was perceived from the gene expression studies. Overall, the current study highlights the tweaking of intricate signaling crosstalk between glycolysis and the Wnt pathway in TNBC cells through inhibiting ENO1 and GLUT4.