Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation.

Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments.

Comprehensive analysis of pseudogene HSPB1P1 and its potential roles in hepatocellular carcinoma.

The incidence and mortality rate of hepatocellular carcinoma (HCC) nowadays is still at high levels. The regulatory roles of pseudogene in cancers have been gradually recognized in recent years. However, comprehensive investigation of abnormally expressed pseudogene and related mechanisms in HCC remains lacking. GSE124535 dataset was used to identify differentially expressed pseudogenes in HCC tissues compared with normal tissues. Prognostic value of these differentially expressed pseudogenes was analyzed at GEPIA. StarBase used to analyze microRNAs (miRNAs) can bind with pseudogene, while the targets for these miRNAs were analyzed at miRTarBase. Protein-protein interaction (PPI) network was then established for miRNA targets, after that hub genes were selected. Expression correlation of pseudogene and hub genes was analyzed at StarBase. In total, 16 upregulated and 17 downregulated pseudogenes were identified. Pseudogene HSPB1P1 was identified abnormally expressed in 20 types of human cancers and could be used as an indicator for poorer overall survival of patients with HCC. Functional analyses showed that HSPB1P1 was strongly correlated with signaling pathways related to cancer progression. Further studied revealed that HSPB1P1 could direct regulate the EZH2 expression in HCC. In summary, our study indicated that HSPB1P1 was a predictor for poorer overall survival of patients with HCC and may be potential therapeutic target against HCC.

Identification of aberrantly expressed lncRNA and the associated TF-mRNA network in hepatocellular carcinoma.

Emerging evidence has indicated that long noncoding RNA (lncRNA) plays crucial roles in regulating hepatocellular carcinoma (HCC) progression. However, a comprehensive lncRNA-transcription factor (TF)-message RNA (mRNA) in HCC remains absent. The aim of this study was to identify aberrantly expressed lncRNAs and the associated TF-mRNA network in HCC. GSE124535 dataset was downloaded from the Gene Expression Omnibus database. Limma package in R was conducted to analyze abnormally expressed lncRNAs (differentially expressed lncRNAs [DELs]) and mRNAs (differentially expressed genes). The prognostic roles of screened DELs in HCC were evaluated at GEPIA. The expression of DELs that correlated with overall survival of HCC patients was further validated using circlncRNAnet, GEPIA, and StarBase. LncRNA-TF-mRNA potential triplets in HCC were predicted by LncMAP. After that, lncRNA-TF-mRNA networks were established using Cytoscape. A total of 20 upregulated and 17 downregulated DELs were identified in HCC tissues compared with adjacent noncancerous tissues. Six out of 20 upregulated and 6 out of 17 downregulated DELs were identified have significant impact on the overall survival of HCC patients. Results in circlncRNAnet, GEPIA, and Starbase confirmed the expression level of DELs obtained from GSE124535. Finally, the LncRNA-TF-mRNA regulatory networks for upregulated and downregulated lncRNA were established based on these analyses results. Among these lncRNAs in the network, the aberrantly expression of lncRNAs including LINC00511, RP11-290F5.1, MIR4435-2HG, and CTC-537E7.3 in HCC was first time reported to date. In the study, potential LncRNA-TF-mRNA regulatory networks were identified, which will advance our understanding concerning the progression of HCC.

Identification of lncRNA-miRNA-mRNA regulatory network associated with epithelial ovarian cancer cisplatin-resistant.

To construct a long noncoding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) regulatory network related to epithelial ovarian cancer (EOC) cisplatin-resistant, differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) between MDAH and TOV-112D cells lines were identified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. Downstream mRNAs or upstream lncRNAs for miRNAs were analyzed at miRTarBase 7.0 or DIANA-LncBase V2, respectively. A total of 485 significant DEGs, 85 DELs, and 5 DEMs were identified. Protein-protein interaction (PPI) network of DEGs contrains 81 nodes and 141 edges was constructed, and 25 hub genes related to EOC cisplatin-resistant were identified. Subsequently, a lncRNA-miRNA-mRNA regulatory network contains 4 lncRNAs, 4 miRNAs, and 35 mRNAs was established. Taken together, our study provided evidence concerning the alteration genes involved in EOC cisplatin-resistant, which will help to unravel the mechanisms underlying drug resistant.

Identification of hub genes to regulate breast cancer metastasis to brain by bioinformatics analyses.

Breast cancer with metastasis especially brain metastasis represents a significant cause of morbidity and mortality in patients. In this study, we aimed to investigate the hub genes and potential molecular mechanism in brain metastasis breast cancer. Expression profiles of the genes were extracted from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichment analyses were conducted at Database for Annotation, Visualization, and Integrated Discovery. Protein-protein interaction (PPI) network was established by STRING database constructed by Cytoscape software. Hub genes were identified by the molecular complex detection (MCODE) plugin and the CytoHubba plugin. The transcription factor (TF) that regulates the expression of hub genes was analyzed using the NetworkAnalyst algorithm. Kaplan-Meier curve was used to analyze the effects of hub genes on overall survival. Two GEO databases (GSE100534 and GSE52604) were downloaded from GEO databases. A total of 102 overlapped genes were identified, and the top five KEGG pathways enriched were pathways in cancer, HTLV-I infection, focal adhesion, ECM-receptor interaction, and protein digestion and absorption. By combing the results of MCODE and CytoHubba, a total of 10 hub genes were selected. Kaplan-Meier curve showed that ANLN, BUB1, TTK, and SKA3 were closely associated with the overall survival of breast cancer patients. TF analysis results showed that E2F4, KDM5B, and MYC were crucial regulators for these four hub genes. The current study based on the GEO database provided novel understanding regarding the mechanism of breast cancer metastasis to brain and may provide novel therapeutic targets.

Highly efficient upconversion luminescence of Er heavily doped nanocrystals through 1530 nm excitation.

Improving luminescence efficiency is of vital importance for applications of rare-earth-doped upconversion materials. Herein, we present highly efficient upconversion nanocrystal, which is brighter than the state-of-the-art Er3+/Yb3+ co-doped core-shell material, through Er3+ heavily doping and 1530 nm excitation. Moreover, upconversion characteristics and mechanisms of Er3+ heavily doped core nanocrystals and their core-shell counterparts are investigated carefully.

Metabolic engineering of Bacillus subtilis for biosynthesis of heparosan using heparosan synthase from Pasteurella multocida, PmHS1.

Heparosan, the capsular polysaccharide discovered in many pathogenic bacteria, is a promising material for heparin preparation. In this study, the Pasteurella multocida heparosan synthase 1 (PmHS1) module was used to synthesize heparosan with controlled molecular weight, while tuaD/gtaB module or gcaD module was responsible for UDP-precursors production in Bacillus subtilis 168. After metabolic pathway optimization, the yield of heparosan was as high as 237.6 mg/L in strain containing PmHS1 module and tuaD/gtaB module, which indicated that these two modules were key factors in heparosan production. The molecular weight of heparosan varied from 39 to 53 kDa, which indicated that heparosan molecular weight could be adjusted by the amount of PmHS1 and the ratio of two UDP precursors. The results showed that it would be possible to produce safe heparosan with appropriate molecular weight which is useful in heparin production.

Systematic training of table tennis players' physical performance based on artificial intelligence technology and data fusion of sensing devices.

This research emphasises the value of physical training for table tennis players, particularly as ball speed and spin rate decline and emphasises how important intensity quality is to the game. Chinese table tennis players' dual identities place greater demands on the general growth of their learning and training as a crucial component of talent development preparation. Athletes' general quality, competitive level, and ability to avoid sports injuries are all improved by scientific and focused physical training. In order to achieve the functions of intelligent camera, multi-angle broadcasting, and 3D scene reproduction, this study combines the physical training model of artificial intelligence. This gives the audience a more engaging and in-depth viewing experience. More feature extraction of the match footage is made possible by deep learning and convolutional neural networks when combined with large-scale video data, greatly enhancing the match information for viewers. The experimental findings demonstrate that the accuracy of table tennis human technical movement recognition reaches 98.88 % based on the enhanced AM-Softmax classification algorithm.

Functional lncRNA-miRNA-mRNA Networks in Response to Baicalein Treatment in Hepatocellular Carcinoma.

INTRODUCTION: Baicalein has been shown to have antitumor activities in several cancer types. However, its acting mechanisms remain to be further investigated. This work is aimed at exploring the functional long noncoding RNA (lncRNA)/microRNA (miRNA)/messenger RNA (mRNA) triplets in response to baicalein in hepatocellular carcinoma (HCC) cell to understand the mechanisms of baicalein in HCC. METHODS: Differentially expressed lncRNAs (DELs) and miRNAs (DEMs) in HCC cell treated with baicalein were first screened using GSE95504 and GSE85511, respectively. miRNA targets for DELs were predicted and intersected with DEMs, after which the miRNA expression was validated using ENCORI and its prognostic value was assessed using Kaplan-Meier plotter. Potential miRNA targets were predicted by 3 prediction tools, after which expression level was validated at UALCAN and Human Protein Atlas. Kaplan-Meier plotter was used to evaluate the effects of these genes on overall survival and recurrence-free survival of HCC patients. Enrichment analyses for these genes were performed at DAVID. RESULTS: Here, we identified 14 overlapping DELs and 26 overlapping DEMs in the baicalein treatment group than those in the DMSO treatment group. Subsequently, by analyzing expression and clinical significance of miRNAs, hsa-miR-4443 was found as a highly potential miRNA target. Then, targets of hsa-miR-4443 were predicted and analyzed, and we found AKT1 was the most potential target for hsa-miR-4443. Hence, the lncRNAs-hsa-miR-4443-AKT1 axis that can respond to baicalein was established. CONCLUSION: Collectively, we elucidated a role of lncRNAs-hsa-miR-4443-AKT1 pathway in response to baicalein treatment in HCC, which could help us understand the roles of baicalein in inhibiting cancer progression and may provide novel insights into the mechanisms behind HCC progression.

Patient-Derived Organoids Can Guide Personalized-Therapies for Patients with Advanced Breast Cancer.

Most breast cancers at an advanced stage exhibit an aggressive nature, and there is a lack of effective anticancer options. Herein, the development of patient-derived organoids (PDOs) is described as a real-time platform to explore the feasibility of tailored treatment for refractory breast cancers. PDOs are successfully generated from breast cancer tissues, including heavily treated specimens. The microtubule-targeting drug-sensitive response signatures of PDOs predict improved distant relapse-free survival for invasive breast cancers treated with adjuvant chemotherapy. It is further demonstrated that PDO pharmaco-phenotyping reflects the previous treatment responses of the corresponding patients. Finally, as clinical case studies, all patients who receive at least one drug predicate to be sensitive by PDOs achieve good responses. Altogether, the PDO model is developed as an effective platform for evaluating patient-specific drug sensitivity in vitro, which can guide personal treatment decisions for breast cancer patients at terminal stage.

Correction: Paclitaxel prodrug based mixed micelles for tumor-targeted chemotherapy.

[This corrects the article DOI: 10.1039/C7RA07796C.].

An artificial cell system for biocompatible gene delivery in cancer therapy.

The gene therapy of cancer is generally recognized as a promising approach for the reversal of neoplastic progress of cancer cells. However, the lack of biocompatibility as well as highly available carriers is the bottleneck in gene therapy. In recent years, with the advances of understanding in cell signaling and cell based functions, the development of cell mimic carriers is showing great potential in evaluating the anticancer efficacy of drugs. Here in our study, an artificial cell (AC) system was fabricated to mimic the gene protection and transfection functions of cancer cells using cancer cell derived histone and membranes. As expected, the AC showed high biocompatibility as well as preferable gene transfection capability both in vitro and in vivo, and might be a promising tool for the flexible assembly of cell mimic systems in cancer therapy.

Analysis of miRNA-mRNA regulatory network revealed key genes induced by aflatoxin B1 exposure in primary human hepatocytes.

BACKGROUND: Aflatoxin B1 (AFB1) exposure is a crucial factor to initiate hepatocellular carcinoma (HCC). However, comprehensive microRNA (miRNA)-message RNA (mRNA) regulatory network regarding AFB1-associated HCC is still lacking. This work was aimed to identify miRNA-mRNA network in primary human hepatocytes after AFB1 exposure. METHODS: A miRNA expression dataset GSE71540 obtained from the gene expression omnibus (GEO) was used to identify differentially expressed miRNAs (DEMs) after AFB1 exposure using GEO2R. Target genes of these DEMs were identified using TargetScan V_7.2, miRDB, PITA, miRanda, and miRTarBase. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed at Database for Annotation, Visualization and Integrated Discovery (DAVID). miRNA-mRNA regulatory network was established by analyzing three enriched KEGG pathways significantly correlated with HCC onset and then visualized at CytoScape. RESULTS: In this work, nine upregulated and nine downregulated DEMs were identified. Functional enrichment analyses showed that these predicted target genes were significantly associated with cancer development. Analysis of three enriched pathways related to the onset of HCC identified 13 and nine target genes for upregulated DEMs and downregulated DEMs, respectively. Subsequently, the miRNA-mRNA regulatory networks were constructed. CONCLUSIONS: In conclusion, miRNA-mRNA regulatory network was established, which will help to understand the mechanism underlying the AFB1-induced onset of HCC.

Facile preparation of biocompatible nanostructured lipid carrier with ultra-small size as a tumor-penetration delivery system.

Insufficient tumor penetration is one of the major obstacles for satisfactory cancer therapy. As a result, the ability to push the lower limits of size for nanoparticle platforms that have traditionally existed in larger forms is highly desirable. In our study, a facile solvent diffusion method was applied to prepare an ultra-small nanostructured lipid carrier (usNLC) which was capable of encapsulating hydrophobic molecules. Our results demonstrate that the as-prepared usNLC is composed of homogeneous particles with size around 25 nm. In addition to its preferable colloidal stability, negligible hemolysis as well as strong tumor homing property, the as-prepared usNLC shows preferable tumor penetration capacity both in vitro and in vivo. The paclitaxel (PTX) loaded usNLC shows comparable in vitro cytotoxicity on HepG2 cells and multicellular tumor spheroids to Taxol with the best in vivo anti-tumor efficacy, which all indicate its potential to be a promising candidate for cancer therapy.

Metabolic engineering of Bacillus subtilis for the efficient biosynthesis of uniform hyaluronic acid with controlled molecular weights.

Bacillus subtilis was engineered into an efficient hyaluronic acid (HA) producer by introducing two inducible artificial operons carrying HA synthase gene from Pasteurella multocida and precursor genes encoding enzymes involved in synthesis of the sugar precursors. A two-stage induction strategy was established for metabolic engineering of recombinant B. subtilis to efficiently produce uniform HA with controlled molecular weights. Strain TPG223 produced larger HA molecules (yield=6.8 g/L; molecular weight=4.5 MDa) than strain PG6181 (yield=2.4 g/L; molecular weight=13 KDa), indicating that the enzymes involved in the synthesis of UDP-glucuronic acid are essential for HA biosynthesis. Strain TPG223 was able to synthesize HA molecules ranging in molecular weight from 8 KDa to 5.4 MDa indicating that size control is achievable in vivo through appropriate tools. The work reported here not only advanced mechanisms research of size control in vivo, but also could be an attractive alternative for commercial preparation of uniform size-defined HA.