Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network

Identification of potential biomarkers from amino acid transporter in the activation of hepatic stellate cells via bioinformatics

Background

The etiopathogenesis of hepatic stellate cells (HSC) activation has yet to be completely comprehended, and there has been broad concern about the interplay between amino acid transporter and cell proliferation. This study proposed exploring the molecular mechanism from amino acid transport-related genes in HSC activation by bioinformatic methods, seeking to identify the potentially crucial biomarkers.

Methods

GSE68000, the mRNA expression profile dataset of activated HSC, was applied as the training dataset, and GSE67664 as the validation dataset. Differently expressed amino acid transport-related genes (DEAATGs), GO, DO, and KEGG analyses were utilized. We applied the protein-protein interaction analysis and machine learning of LASSO and random forests to identify the target genes. Moreover, single-gene GESA was executed to investigate the potential functions of target genes via the KEGG pathway terms. Then, a ceRNA network and a drug-gene interaction network were constructed. Ultimately, correlation analysis was explored between target genes and collagen alpha I (COL1A), alpha-smooth muscle actin (α-SMA), and immune checkpoints.

Results

We identified 15 DEAATGs, whose enrichment analyses indicated that they were primarily enriched in the transport and metabolic process of amino acids. Moreover, two target genes (SLC7A5 and SLC1A5) were recognized from the PPI network and machine learning, confirmed through the validation dataset. Then single-gene GESA analysis revealed that SLC7A5 and SLC1A5 had a significant positive correlation to ECM-receptor interaction, cell cycle, and TGF-β signaling pathway and negative association with retinol metabolism conversely. Furthermore, the mRNA expression of target genes was closely correlated with the COL1A and α-SMA, as well as immune checkpoints. Additionally, 12 potential therapeutic drugs were in the drug-gene interaction network, and the ceRNA network was constructed and visualized.

Conclusion

SLC7A5 and SLC1A5, with their relevant molecules, could be potentially vital biomarkers for the activation of HSC.

Identifying vital nodes for yeast network by dynamic network entropy

BACKGROUND

The progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast.

RESULTS

Considering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor.

CONCLUSIONS

It is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

Multi-task aquatic toxicity prediction model based on multi-level features fusion

INTRODUCTION

With the escalating menace of organic compounds in environmental pollution imperiling the survival of aquatic organisms, the investigation of organic compound toxicity across diverse aquatic species assumes paramount significance for environmental protection. Understanding how different species respond to these compounds helps assess the potential ecological impact of pollution on aquatic ecosystems as a whole. Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability.

OBJECTIVES

This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity.

METHODS

The model integrates molecular fingerprints and molecule graphs to characterize molecules, enabling the simultaneous prediction of acute toxicity for the same organic compound across four distinct fish species. Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. We employ cross-validation in our experiments to assess the performance and generalization ability of ATFPGT-multi.

RESULTS

The experimental results indicate, first, that ATFPGT-multi outperforms ATFPGT-single on four fish datasets with AUC improvements of 9.8%, 4%, 4.8%, and 8.2%, respectively, demonstrating the superiority of multi-task learning over single-task learning. Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. Moreover, ATFPGT-multi utilizes attention scores to identify molecular fragments associated with fish toxicity in organic molecules, as demonstrated by two organic molecule examples in the main text, demonstrating the interpretability of ATFPGT-multi.

CONCLUSION

In summary, ATFPGT-multi provides important support and reference for the further development of aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/ATFPGT-multi.

New perspectives on the potential of tetrandrine in the treatment of non-small cell lung cancer: bioinformatics, Mendelian randomization study and experimental investigation

BACKGROUND

Although there are numerous treatment methods for NSCLC, long-term survival remains a challenge for patients. The objective of this study is to investigate the role and causal relationship between the target of tetrandrine and non-small cell lung cancer (NSCLC) through transcriptome and single-cell sequencing data, summary-data-based Mendelian Randomization (SMR) and basic experiments. The aim is to provide a new perspective for the treatment of NSCLC.

METHODS

We obtained the drug target gene of tetrandrine through the drug database, and then used the GSE19188 data set to obtain the NSCLC pathogenic gene, established a drug-disease gene interaction network, screened out the hub drug-disease gene, and performed bioinformatics and tumor cell immune infiltration analysis. Single-cell sequencing data (GSE148071) to determine gene location, SMR to clarify causality and drug experiment verification.

RESULTS

10 drug-disease genes were obtained from 213 drug targets and 529 disease genes. DO/GO/KEGG analysis showed that the above genes were all related to the progression and invasion of NSCLC. Four drug-disease genes were identified from a drug-disease PPI network. These four genes were highly expressed in tumors and positively correlated with plasma cells, T cells, and macrophages. Subsequent single-cell sequencing data confirmed that these four genes were distributed in epithelial cells, and SMR analysis revealed the causal relationship between CCNA2 and CCNB1 and the development of NSCLC. The final molecular docking and drug experiments showed that CCNA2 and CCNB1 are key targets for tetrandrine in the treatment of NSCLC.

Exploring the anticancer potential of Eleutherine bulbosa: A systematic network pharmacology study on lung cancer

Chemotherapy application in lung cancer patients has several side effects and shows lower effectiveness due to chemoresistance. Although Eleutherine bulbosa (Mill.) Urb. (EBE) elicit anticancer properties, yet the exact profile of its active compounds and lung cancer inhibition mechanisms were not fully understood. This study aimed to identify suggestive compounds from EBE extract and explain the molecular mechanisms of EBE against lung cancer. Identification of the compound from the EBE extract was confirmed using liquid chromatography-tandem mass spectrophotometry (LC-MS/MS). The bioavailability profile of three major metabolites was identified using absorption, distribution, metabolism, excretion, toxicity software. The anticancer molecular mechanism prediction of the drugs was ascertained by network pharmacology using Cytoscape 3.9.1 and the protein-protein interaction network technique with STRING 11.0. Interaction between resveratrol and extracellular growth factor receptor (EGFR) was analyzed using site-specific molecular docking with erlotinib as the control using PyRx Autodock Vina 9.0 and BIOVIA Discovery Studio. A total of 16 active compounds were identified from LC-MS/MS. Only resveratrol showed anticancer properties by its interaction with 13 genes and 6 signaling pathways related to lung cancer. The molecular docking result supports the network pharmacology finding. The binding affinity of resveratrol with EGFR, important receptor in lung cancer, was more negative (-6.9 kcal/mol) than erlotinib (-6.2 kcal/mol) as the control. Evidence suggested that resveratrol in EBE exhibits anticancer effects by modulating lung cancer cell proliferation and apoptosis through EGFR binding.

Identification of 3-(5-cyano-6-oxo-pyridin-2-yl)benzenesulfonamides as novel anticancer agents endowed with EGFR inhibitory activity

New 5-cyano-6-oxo-pyridine-based sulfonamides (6a-m and 8a-d) were designed and synthesized to potentially inhibit both the epidermal growth factor receptor (EGFR) and carbonic anhydrase (CA), with anticancer properties. First, the in vitro anticancer activity of each target substance was tested using Henrietta Lacks cancer cell line and M.D. anderson metastasis breast cancer cell line cells. Then, the possible CA inhibition against the human CA isoforms I, II, and IX was investigated, together with the EGFR inhibitory activity, with the most powerful derivatives. The neighboring methoxy group may have had a steric effect on the target sulfonamides, which prevented them from effectively inhibiting the CA isoforms while effectively inhibiting the EGFR. The effects of the 5-cyanopyridine derivatives 6e and 6l on cell-cycle disruption and the apoptotic potential were then investigated. To investigate the binding mechanism and stability of the target molecules, thorough molecular modeling assessments, including docking and dynamic simulation, were performed.

Novel 4-(2-arylidenehydrazineyl)thienopyrimidine derivatives as anticancer EGFR inhibitors: Design, synthesis, biological evaluation, kinome selectivity and in silico insights

The current study discovered fifteen new thieno[2,3-d]pyrimidine derivatives with potential anticancer action, including 5a-l, 6, and 7a-b. Results from the NCI screening revealed that compounds 5f-i and 7a significantly inhibited the proliferation of MDA-MB-468 cells at mean GI% and GI50 levels. Compared to staurosporine, these compounds (5f-i and 7a) demonstrated better safety towards typical WI-38 cells. Compounds 5g and 7a demonstrated the highest inhibition (two-digit nanomolar) when compared to erlotinib when their potency was tested on EGFR kinase. Considering the outcomes above, 5g was examined for its ability to disrupt the cell cycle with trigger apoptosis in breast cancer MDA-MB-468 cell lines. The apoptosis markers Bax, Bcl-2, Caspase-8, and Caspase-9, were detected. In silico molecular docking and dynamic simulation were used to explainthe biological activities of the most potent compound.