Combinatorial Chemistry & High Throughput Screening - Volume 25, Issue 13, 2022

Multi-drug resistance (MDR) is characterized by the resistance of tumor cells to some antitumor drugs with different structures and mechanisms after the use of a single chemotherapy drug or even the first use of the drug. Notably, MDR has become the largest obstacle to the success of gastric cancer chemotherapies. Non-coding RNAs are defined as a class of RNAs that do not have the ability to code proteins. They are widely involved in important biological functions in life activities. Multiple lines of evidence demonstrated that ncRNAs are closely related to human cancers, including gastric cancer. However, the relationship between ncRNAs and MDR in gastric cancer has been reported, yet the mechanisms are not fully clarified. Therefore, in this review, we systematically summarized the detailed molecular mechanisms of lncRNAs (long noncoding RNAs) and miRNAs (microRNAs) associated with MDR in gastric cancer. Additionally, we speculate that the abnormal expression of ncRNAs is likely to be a novel potential therapeutic target reversing MDR for gastric cancer. Future therapeutics for gastric cancer will most likely be based on noncoding RNAs (ncRNAs) that regulate MDR-related genes.

Cancer is a vast form of the disease that can begin in almost any organ or tissue of the body when abnormal cells grow uncontrollably and attack nearby organs. The traditional approaches to cancer diagnosis and drug development have certain limitations, and the outcomes achieved through the traditional approaches applied to cancer diagnosis and drug development are not quite promising. Artificial intelligence is not new to the medical research sector. AI-based algorithms hold great potential for identifying mutations and abnormal cell division at the initial stage of cancer. Advanced researchers are also focusing on bringing AI to clinics in a safe and ethical manner. Early cancer detection saves lives and is critical in the fight against the disease. As a result, as part of earlier detection, computational approaches such as artificial intelligence have played a significant role in cancer diagnosis and drug development.

Background: Ermiao San, one of the Chinese medicine formulas, has been widely used to treat rheumatoid arthritis (RA). Our previous study has demonstrated that Ermiao San is effective in treating RA. However, its pharmacological mechanisms remain unclear. Therefore, the purpose of this study was to decipher the potential mechanism of action of Ermiao San in rheumatoid arthritis (RA) by bioinformatics, network pharmacology, molecular docking, and molecular dynamics. Methods: Gene expression data (GSE77298) were obtained from the GEO database. Differentially expressed genes (DEGs) were analyzed by R. The active ingredients of Huangbai (Phellodendron) and Cangshu (Atractylodes), two main constituents of Ermiao San, and their predicted target genes were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the overlapping genes between DEGs of the RA dataset and the predicted target genes of Ermiao San. The gene-gene interaction network was analyzed and visualized by Cytoscape. Molecular docking and dynamics simulations were performed to study the interaction between selected target genes (Chemokine ligand 2 (CCL2) and matrix metalloproteinase 1 (MMP1)) and active ingredients (quercetin and wogonin) of Ermiao San. Results: A total of 16 potential targets for Ermiao San were identified, with significantly enriched GO terms, such as cytokine-mediated signaling pathways, oxidoreductase activity, cell space, etc., and IL-17 signaling pathway, rheumatoid arthritis pathway, and NF-ΚB signaling pathway were identified as enriched pathways through KEGG analysis. CCL2 and MMP1 were identified and verified to be the targets of both quercetin and wogonin, the two active ingredients of Ermiao San, by molecular docking and molecular dynamics. Conclusion: Ermiao San may target CCL2 and MMP1 via its active ingredients by exerting therapeutic effects on RA.

Aim: This study aims to comprehensively analyse the Ribosomal Protein S6 Kinase A4 (RPS6KA4) and determine the prognostic value for hepatocellular carcinoma (HCC). Background: Liver cancer is a common type of tumor worldwide, and HCC accounts for about 75 to 85% of all primary liver cancer cases. The Ribosomal S6 protein kinases (RSK) family plays an important regulatory role in cell growth, movement, survival, and proliferation. Methods: We collected the expression and clinicopathological features of RPS6KA4 in The Cancer Genome Atlas (TCGA) cohort and evaluated the prognostic value of RPS6KA4 in HCC. Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were performed to determine the enrichment pathways of RPS6KA4. Correlation between RPS6KA4 expression and immune infiltration was analyzed. Protein-protein interaction (PPI) network analysis was performed to screen hub genes. Results: RPS6KA4 overexpression is statistically significant in HCC relative to normal tissues (P < 0.001). Increased expression of RPS6KA4 is associated with higher T stage (p=0.021), pathological stage (p=0.006), α-fetoprotein (AFP) value (p=0.026), and vascular invasion (p=0.023) of HCC. Overexpression of RPS6KA4 predicted worse overall survival (OS, P=0.002), disease-specific survival (DSS, P=0.012), and progress-free interval (PFI, P=0.031) for HCC. Univariate/multivariate Cox regression analysis confirmed that RPS6KA4 was an independent risk factor for HCC (P=0.002 in univariate analysis; P=0.014 in multivariate analysis). GO/KEGG analysis and GSEA analysis suggest that RPS6KA4 plays a precancer role in HCC through epigenetics, cell adhesion, tumor-driven GTPase pathways, infection-related carcinogenesis, and adaptive immunity. Immune infiltration analysis confirmed the strong negative relationship between RPS6KA4 and B cells, CD4+ T cells, macrophages, neutrophils, as well as dendritic cells. Protein-protein interactions (PPI) analysis and hub gene identification revealed the cancer-promoting effects of RPS6KA4 related to RSKs, AP-2, clathrin, and MAPK/ ERK pathways. Conclusion: RPS6KA4 is a potentially valuable molecule for understanding HCC tumorigenesis. Increased RPS6KA4 might be a promising prognostic factor for low HCC survival.

Background: Fidgetin-like 1 (FIGNL1), a subfamily member of ATPases, is associated with diverse cellular activities (AAA proteins). FIGNL1 is involved in DNA repair. However, the latest study has indicated that FIGNL1 plays a crucial role in the occurrence and development of malignant tumors. Methods: FIGNL1 expression was analyzed via Oncomine and GEPIA databases, and its prognostic potential was analyzed using OncoLnc, UALCAN, and GEPIA databases. Moreover, the promoter methylation of FIGNL1 was analyzed through the UALCAN database. FIGNL1-related gene network was found within STRING. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were investigated across WebGestalt. FIGNL1 correlation with cancer immune infiltrates was estimated using the Tumor Immune Estimation Resource (TIMER) database. Results: We found that FIGNL1 is widely overexpressed in multiple human cancers, and its high expression was correlated with the poor prognosis of patients with kidney renal clear-cell carcinoma (KIRP), low-grade glioma (LGG) of brain and liver hepatocellular carcinoma (LIHC). Additionally, the promoter methylation level of FIGNL1 showed a statistical significance between normal and primary tissues in KIRP and LGG via the UALCAN (P < 0.0001). FIGNL1 expression was highly correlated with the infiltrating levels of CD8+ T and CD4+ T cells, dendritic cells (DCs), macrophages, and neutrophils in LIHC. Conclusions: In this study, the correlation of FIGNL1 expression with the prognosis, promoter methylation, and immune infiltrates in KIRP, LGG, and LIHC was revealed. These findings suggested that FIGNL1 promised to be a prognostic biomarker for KIRP, LGG, and LIHC.

Background and Objective: To explore the molecular mechanism by which Shengmaiyin (Codonopsis pilosula) (SMY) improves isoproterenol (ISO)-induced heart failure (HF) in rats via a traditional Chinese medicine (TCM) integrated pharmacology research platform, The Chinese Medicine Integrated Pharmacology Platform (TCMIP V2.0). Method: The chemical constituents and drug targets of SMY medicines were identified through TCMIP, and HF disease target information was collected. A prescription Chinese medicinecomponent- core target network was constructed through the TCM network mining module, and biological process and pathway enrichment analyses of core targets were conducted. In vivo experiments in rats were performed to verify the pathway targets. Hematoxylin and eosin staining was used to observe myocardial tissue morphology. ELISA kits were used to detect cAMP content, and Western blotting was used to detect the expression levels of signaling pathway-related proteins. Results: The TCMIP analysis indicated that SMY treatment of HF activates the GS-β-adrenergic receptor (βAR)-cAMP-protein kinase A (PKA) signaling pathway. The in vivo experimental results confirmed this finding. High-dose SMY significantly improved the morphology of ISO-injured myocardium. The levels of G-protein-coupled receptor (GPCR), adenylate cyclase (AC), βAR, and PKA proteins in myocardial tissue were significantly increased in the SMY group. In addition, the content of cAMP in myocardial tissue was increased, and the content of cAMP in serum was decreased. Conclusion: Based on the analysis of TCMIP, SMY treatment of HF may activate the GS-βARcAMP- PKA signaling pathway. The findings provide a theoretical basis for further research on the anti-HF mechanism of SMY.

Background: Hepatocellular carcinoma (HCC) is inflammation-associated cancer with high incidence and poor prognosis. In the last decade, immunotherapy has become an important strategy for managing HCC. Objective: This study aimed to establish an immune-related gene signature for predicting prognosis and immunotherapy response in HCC. Methods: We identified immune-related differentially expressed genes (IRDEGs) based on The Cancer Genome Atlas (TCGA) database and the Immunology Database and Analysis Portal (ImmPort) database. The weighted gene co-expression network analysis (WGCNA) and Cox proportional hazard model were utilized to determine hub immune-related genes (IRGs). The TIDE tool and R package pRRophetic were used to assess the correlation between the immune-related gene signature and the clinical responses to immunotherapy and chemotherapy. Results: By using WGCNA combined with Cox proportional hazard model, PRC1, TOP2A, TPX2, and ANLN were identified as hub IRGs. The prognostic value of the newly developed gene signature (IRGPI) was demonstrated in both the TCGA database and the Gene Expression Omnibus (GEO) database. The TIDE tool showed that the high- and low-IRGPI groups presented significantly different tumor immune microenvironment and immunotherapy responses. Furthermore, the high-IRGPI group also had significantly lower chemoresistance to cisplatin than the low-IRGPI group. Conclusion: The IRGPI is a tool for predicting prognosis as well as responsiveness to immunotherapy and chemotherapy in HCC.

Background: Immune-related long noncoding RNAs (lncRNAs) play an important role in the development of cancer. This study aimed to identify immune-related lncRNAs in thyroid cancer (THCA) and develop a prognostic model for THCA. Methods: We downloaded immune-related gene sets from the Gene Set Enrichment Analysis (GSEA) website and obtained THCA gene expression and clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were then obtained by performing correlation analysis on the expression of lncRNAs and immune-related genes. A prognostic model for THCA immune-related lncRNAs was developed through univariate Cox regression and multiple Cox regression analyses. We confirmed the results in clinical samples using quantitative real-time PCR. Results: A total of 26 immune-related lncRNAs in THCA were obtained. Then we constructed a prognosis model composed of seven lncRNAs (LINC01614, AC017074.1, LINC01184, LINC00667, ACVR2B-AS1, AC090673.1, and LINC00900). Our model can be used as an independent prognostic factor. Principal component analysis displayed that the lncRNAs in the model can distinguish between high and low-risk groups. Clinical correlation analysis showed that the expression levels of AC090673.1 (P<0.05), LINC01184 (P<0.001), and LINC01614 (P<0.001) were related to disease stage, and LINC00900 (P<0.001) and LINC01614 (P<0.001) were related to T stage. We validated this model in cancer and paracancerous tissues from 24 THCA patients. Conclusion: We identified and experimentally validated seven immune-related lncRNAs that can serve as potential biomarkers for THCA prognosis.

Background: Septic shock is a frequent and costly problem among patients in the pediatric intensive care unit (PICU) and is associated with high mortality and devastating survivor morbidity. In this study, we aimed to screen candidate biomarkers and potential therapeutic targets for septic shock. Methods: GSE26440 dataset was downloaded from Gene Expression Omnibus (GEO), including 32 normal controls and 98 children with septic shock RNA samples from whole blood. The pathways and functional annotations of differentially expressed genes (DEGs) in the two types of samples were examined by GO and KEGG pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Protein-protein interactions (PPI) of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING) and Hub gene identification was performed by the plug-in cytoHubba in Cytoscape software. Results: A total of 140 genes were identified as DEGs, of which 98 genes were up-regulated and 42 genes were down-regulated. GO function analysis showed that DEGs were significantly enriched in biological processes, including immune response, leukocyte activation involved in immune response, and so on. The top hub genes, namely MMP9, CEACAM8, ARG1, MCEMP1, LCN2, RETN, S100A12, GPR97, and TRAT1 were recognized from the protein-protein interaction (PPI) network. Furthermore, qRT-PCR results demonstrated that the mRNA level of MMP9, CEACAM8, ARG1, MCEMP1, LCN2, RETN, and S100A12 was elevated while GPR97 was decreased in involved mouse and human models. However, TRAT1 expression is species-dependent which was decreased in the mouse septic shock model but elevated in the human LPS-treated macrophages model. Conclusion: Taken together, the identification and validation of several novel hub genes, especially GPR97 and TRAT1, deepen our comprehension of the molecular mechanisms of septic shock progression. These genes may be therapeutic molecular targets or diagnostic biomarkers in patients with septic shock.

Objective: The potential pathogenesis of LUAD remains largely unknown. In the present study, we evaluated the competing endogenous RNA (ceRNA) regulatory network and tumorinfiltrating immune cells in LUAD. Methods: We obtained the RNA profiles and corresponding clinical information of LUAD patients from the TCGA data portal, and identified differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between LUAD samples and normal controls to build a ceRNA network. Additionally, the CIBERSORT algorithm was employed to analyze the patterns of immune cell infiltration. Then, two survival-predicting models were constructed based on the ceRNA network and tumor-infiltrating immune cells, which were validated by an independent GEO dataset GSE50081. Moreover, the correlation between prognosis-related ceRNAs and immune cells was also evaluated. Results: In total, 484 LUAD samples and 59 normal controls were included in this study, and 15 DEmiRNAs, 94 DEmRNAs, and 7 DElncRNAs were integrated to construct the ceRNA network of LUAD. Meanwhile, differentially expressed tumor-infiltrating immune cells were also identified, and the expressions of monocytes and regulatory T cells were related to the overall survival (OS) of LUAD patients. Moreover, the prognostic prediction model based on ceRNA network or tumor-infiltrating immune cells exhibited significant power in predicting the survival of LUAD patients. Furthermore, co-expression analysis revealed that some prognosis-related ceRNAs, such as CCT6A, E2F7, SLC16A1, and SNHG3, were positively or negatively correlated with several tumorinfiltrating immune cells, such as monocytes and M1 macrophages. Conclusion: This study improves our understanding of the pathogenesis of LUAD and is helpful in exploring the potential therapeutic targets and prognostic biomarkers for LUAD.

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous malignant tumor. Therefore, it is necessary to find predictive biomarkers related to the prognosis and immune infiltration of lung adenocarcinoma, which may provide an effective theoretical basis for its clinical treatment. Objective: This study aimed to evaluate whether the expression level of PHD3 in lung adenocarcinoma (LUAD) is related to immunity. Methods: PHD3 expression was analyzed by the ONCOMINE, TIMER, UALCAN, and GEPIA databases. The correlations between clinical information and PHD3 expression were analyzed by the LinkedOmics database. Then, we evaluated the influence of PHD3 on the survival of LUAD patients using Kaplan-Meier Plotter and HPA database. We explored the correlation between PHD3 and tumor immunity using TIMER and the correlation module of TISDIB. Finally, we used the cBioportal database to analyze PHD3 mutations in LUAD. Results: Comprehensive analysis displayed PHD3 expression to be clearly higher in LUAD compared to adjacent normal tissues. PHD3 expression was identified to be positively associated with tumor purity, histological type, and later pathological stage. Survival curve results revealed the high expression of PHD3 in LUAD patients to be accompanied by a poor prognosis. Further study indicated PHD3 to be significantly related to a variety of tumor immune cells and molecules. Moreover, among the LUAD cases with gene alteration of PHD3, amplification was the most common of all alteration types. Conclusion: PHD3 may be used as a biomarker for survival and immunotherapy of LUAD.

Background: A xiaoqinglong decoction (XQLD) has been proven effective in treating severe coronavirus disease 2019 (COVID-19) cases; however, the mechanism remains unclear. Objective: In the current study, we used network pharmacology and molecular docking technology to identify the effective components, potential targets, and biological pathways of XQLD against COVID-19. Methods: Public databases were searched to determine the putative targets of the active compounds of XQLD and COVID-19-related targets. STRING and Cytoscape were used to establish the protein-protein interaction network and drug component, along with the target-pathway network. The DAVID database was used to enrich the biological functions and signaling pathways. AutoDock Vina was used for virtual docking. Results: We identified 138 active compounds and 259 putative targets of XQLD. Biological network analysis showed that quercetin, beta-sitosterol, kaempferol, stigmasterol, and luteolin may be critical ingredients of XQLD, whereas VEGFA, IL-6, MAPK3, CASP3, STAT3, MAPK1, MAPK8, CASP8, CCL2, and FOS may be candidate drug targets. Enrichment analysis illustrated that XQLD could function by regulating viral defense, inflammatory response, immune response, and apoptosis. Molecular docking results showed a high affinity between the critical ingredients and host cell target proteins. Conclusion: This study uncovered the underlying pharmacological mechanism of XQLD against COVID-19. These findings lay a solid foundation for promoting the development of new drugs against severe acute respiratory syndrome coronavirus-2 infection and may contribute to the global fight against the COVID-19 pandemic.

Background and Objective: SPC24 was reported to be correlated with the development of many cancers. However, its role in renal cancer was unclear. Our aim was to explore the role of SPC24 in kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) in types of renal cancer. Methods: SPC24 expressions in KIRC and KIRP were firstly analyzed. Subsequently, the correlation between SPC24 expression and TNM staging of KIRC and KIRP and the accuracy of SPC24 in diagnosing KIRC and KIRP were explored. Moreover, the correlation between SPC24 expression and prognosis of KIRC and KIRP were analyzed. Univariate and multivariate analyses were performed to identify prognostic factors in KIRC and KIRP, and nomograms were constructed. The correlation between SPC24 expression and immune cell infiltration, immune molecules, microsatellite instability (MSI), and tumor mutational burden (TMB) were further explored. Finally, the correlations between SPC24 expression and prognosis of KIRC based on different immune cell enrichment were analyzed. Results: SPC24 was significantly up-regulated in multiple cancers, especially KIRC and KIRP. SPC24 expression was significantly correlated with the TNM stage of KIRC and KIRP, and upregulated SPC24 suggested a worse prognosis. Besides, SPC24 possesses good accuracy in diagnosing KIRC and KIRP. The SPC24-based nomograms displayed satisfactory efficacy in KIRC and KIRP. Moreover, we found that SPC24 expression was closely correlated with immune cell infiltration, immune molecules, and TMB in KIRC, and up-regulated SPC24 revealed poor prognosis based on different immune cell enrichment. Conclusion: SPC24 has the potential to be a biomarker predicting the prognosis and/or immune infiltration of KIRC and KIRP.

Objective: The present study aims to assess a proposed treatment approach or therapy for periodontitis by using the in-silico technique. The proposed treatment strategy offers a singular vehicular system consisting of minocycline (antibiotic), celecoxib (selective COX-II inhibitor), doxycycline hyclate (matrix metalloproteinase inhibitor), and hydroxyapatite (osteogenic agent). Material and Methods: Molecular docking studies of drugs were performed using Maestro version 9.4 software Schrödinger, and 3-Dimensional Crystallographic X-ray protein structures of targeted proteins were downloaded from RCSB protein data bank in .pdb file format. These agents were docked, and their affinities towards the receptors/protein/enzyme were calculated. Furthermore, their affinities were compared with the standard drug. Results: The study suggests that minocycline and metronidazole possess equal affinity towards the RGPB and Inlj protein of P.gingivalis. Celecoxib, a well-known inhibitor of the COX-II enzyme, showed very high affinity. Selective inhibitor of MMP-8 possessed higher affinity than doxycycline, whereas CMT-3 showed equal affinity as doxycycline for MMP-13. Similarly, hydroxyapatite and simvastatin also showed a comparatively similar affinity for osteopontin receptor. Conclusion: Based upon molecular docking results, it can be concluded that the proposed treatment strategy would be a suitable approach for periodontitis and all the selected therapeutic agents have potential similar to the standard drugs, thereby constituting a reliable system for periodontitis.