Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy

Gelsolin knockdown confers radiosensitivity to glioblastoma cells

OBJECTIVE

Radiotherapy (RT) is a cornerstone of the glioblastoma (GBM) treatment. However, the resistance of tumour cells to radiation results in early recurrence. The mechanisms underlying GBM radioresistance remain unclear. Screening for differentially expressed genes (DEGs) related to radiation might be a potential solution to this problem.

METHOD

RT-associated DEGs were screened based on the RNA sequencing of 15 paired primary and recurrent GBMs. The mRNA and protein expression of candidate genes were validated in RNA sequencing of The Chinese Genome Atlas (CGGA) dataset and 18 cases of GBM samples. The relationship between the candidate gene and radiation was confirmed in irradiated GBM cells. The association of candidate gene with clinical characteristics and survival was investigated in the CGGA and TCGA dataset. Biological function and pathway analysis were explored by gene ontology analysis. The association of the candidate gene with radiosensitivity was verified using cell counting Kit-8, comet, and colony formation assays in vitro and subcutaneous tumour xenograft experiments in vivo.

RESULTS

Gelsolin (GSN) was selected for further study. GSN expression was significant elevated in recurrent GBM and up-regulated in irradiated GBM cell lines. High expression of GSN was enriched in malignant phenotype of glioma. Moreover, high expression of GSN was associated with poor prognosis. Further investigation demonstrated that GSN-knockdown (GSN-KD) combined with RT significantly inhibited cell proliferation and enhanced radiosensitivity in vivo and in vitro. Mechanistically, GSN-KD could lead to more serious DNA damage and promotes apoptosis after RT.

CONCLUSION

Radiation induced up-regulated of GSN. GSN-KD could enhance the radiosensitivity of GBM.

Searching for Essential Genes and Targeted Drugs Common to Breast Cancer and Osteoarthritis

BACKGROUND

It is documented that osteoarthritis can promote the progression of breast cancer (BC).

OBJECTIVE

This study aims to search for the essential genes associated with breast cancer (BC) and osteoarthritis (OA), explore the relationship between epithelial-mesenchymal transition (EMT)- related genes and the two diseases, and identify the candidate drugs.

METHODS

The genes related to both BC and OA were determined by text mining. Protein-protein Interaction (PPI) analysis was carried out, and as a result, the exported genes were found to be related to EMT. PPI and the correlation of mRNA of these genes were also analyzed. Different kinds of enrichment analyses were performed on these genes. A prognostic analysis was performed on these genes for examining their expression levels at different pathological stages, in different tissues, and in different immune cells. Drug-gene interaction database was employed for potential drug discovery.

RESULTS

A total number of 1422 genes were identified as common to BC and OA and 58 genes were found to be related to EMT. We found that HDAC2 and TGFBR1 were significantly poor in overall survival. High expression of HDAC2 plays a vital role in the increase of pathological stages. Four immune cells might play a role in this process. Fifty-seven drugs were identified that could potentially have therapeutic effects.

CONCLUSION

EMT may be one of the mechanisms by which OA affects BC. Using the drugs can have potential therapeutic effects, which may benefit patients with both diseases and broaden the indications for drug use.

Oral Mucosa in Cancer Patients-Putting the Pieces Together: A Narrative Review and New Perspectives

The oral mucosa is a key player in cancer patients and during cancer treatment. The increasing prevalence of cancer and cancer-therapy-associated side effects are behind the major role that oral mucosa plays in oncological patients. Oral mucositis is a debilitating severe complication caused by the early toxicity of chemo and/or radiotherapy that can restrict treatment outcome possibilities, even challenging a patient's survival. It has been referred to as the most feared cancer treatment complication. Predictive variables as to who will be affected, and to what extent, are still unclear. Additionally, oral mucositis is one of the sources of the increasing economic burden of cancer, not only for patients and their families but also for institutions and governments. All efforts should be implemented in the search for new approaches to minimize the apparently ineluctable outburst of oral mucositis during cancer treatment. New perspectives derived from different approaches to explaining the interrelation between oral mucositis and the oral microbiome or the similarities with genitourinary mucosa may help elucidate the biomolecular pathways and mechanisms behind oral mucosa cancer-therapy-related toxicity, and what is more important is its management in order to minimize treatment side effects and provide enhanced cancer support.

Identification of potential molecular mechanisms and candidate drugs for radiotherapy- and chemotherapy-induced mucositis

BACKGROUND

Radiotherapy-induced oral mucositis (RIOM) and chemotherapy-induced oral mucositis (CIOM) are common complications in cancer patients, leading to negative clinical manifestations, reduced quality of life, and unsatisfactory treatment outcomes.

OBJECTIVE

The present study aimed to identify potential molecular mechanisms and candidate drugs by data mining.

METHODS

We obtained a preliminary list of genes associated with RIOM and CIOM. In-depth information on these genes was explored by functional and enrichment analyses. Then, the drug-gene interaction database was used to determine the interaction of the final enriched gene list with known drugs and analyze the drug candidates.

RESULTS AND CONCLUSION

This study identified 21 hub genes that may play an important role in RIOM and CIOM, respectively. Through our data mining, bioinformatics survey, and candidate drug selection, TNF, IL-6, and TLR9 could play an important role in disease progression and treatment. In addition, eight candidate drugs (olokizumab, chloroquine, hydroxychloroquine, adalimumab, etanercept, golimumab, infliximab, and thalidomide) were selected by the drug-gene interaction literature search additionally, as candidates for treating RIOM and CIOM.

NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction

Prediction of drug-target interactions (DTIs) plays a significant role in drug development and drug discovery. Although this task requires a large investment in terms of time and cost, especially when it is performed experimentally, the results are not necessarily significant. Computational DTI prediction is a shortcut to reduce the risks of experimental methods. In this study, we propose an effective approach of nonnegative matrix tri-factorization, referred to as NMTF-DTI, to predict the interaction scores between drugs and targets. NMTF-DTI utilizes multiple kernels (similarity measures) for drugs and targets and Laplacian regularization to boost the prediction performance. The performance of NMTF-DTI is evaluated via cross-validation and is compared with existing DTI prediction methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR). We evaluate our method on four gold standard datasets, comparing to other state-of-the-art methods. Cross-validation and a separate, manually created dataset are used to set parameters. The results show that NMTF-DTI outperforms other competing methods. Moreover, the results of a case study also confirm the superiority of NMTF-DTI.

A Four-Gene Signature Associated with Radioresistance in Head and Neck Squamous Cell Carcinoma Identified by Text Mining and Data Analysis

Purpose

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer globally, and radiotherapy plays a crucial part in its treatment. This study was designed to identify potential genes related to radiation resistance in HNSCC.

Method

We first used text mining to obtain common genes related to radiotherapy resistance and HNSCC in published articles. Functional enrichment analyses were conducted to identify the significantly enriched pathways and genes. Protein and protein interactions were performed, and the most significant gene modules were determined; then, genes in the gene modules were validated at transcriptional levels and overall survival. Gene set variation analysis (GSVA) score was calculated, and the association between GSVA score and survival/pathway was estimated. Immune cell infiltration, methylation, and genetic alteration analysis of these genes was conducted in HNSCC patients. Finally, potential sensitive anticancer drugs related to target genes were obtained.

Result

We identified 583 common genes through text mining. After further validation, a four-gene signature (EPHB2, SPP1, SERPINE1, and VEGFC) was constructed. The patients with higher GSVA scores have a worse prognosis than those with lower GSVA scores. Differences in methylation of these four genes in HNSCC tumor tissue and normal tissue were compared, with higher methylation levels of EBPH2 and SPP1 in normal tissue and higher methylation levels of SERPINE1 in the tumor. Immune cell infiltration revealed that the increased expression of these genes was closely related to the infiltration level of CD4+ T cell, neutrophil, macrophage, and dendritic cell. Thirty drugs, including 22 positively and eight negatively correlated drugs that most correlated with related genes, were available for treating HNSCC.

Conclusion

In this study, we identified four potential genes as well as corresponding drugs that might be related to radioresistance in HNSCC patients. These candidate genes may provide a promising avenue to further elevate radiotherapy efficacy.

Searching for essential genes and drug discovery in breast cancer and periodontitis via text mining and bioinformatics analysis

The primary purpose of the study was (1) to search for the essential genes associated with breast cancer and periodontitis, and (2) to identify candidate drugs targeted to these genes for expanding the potential drug indications. The genes related to both breast cancer and periodontitis were determined by text mining. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed on these genes, and protein-protein interaction analysis was carried out to export significant module genes. Drug-gene interaction database was employed for potential drug discovery. We identified 221 genes common to both breast cancer and periodontitis. The top six significant enrichment terms and 15 enriched signal pathways were selected. Among 24 significant genes demonstrated as a gene cluster, we found SERPINA1 and TF were significantly related to poor overall survival between the relatively high and low groups in patients. Using the final two genes, 12 drugs were identified that had potential therapeutic effects. SERPINA1 and TF were screened out as essential genes related to both breast cancer and periodontitis, targeting 12 candidate drugs that may expand drug indications. Drug discovery using text mining and analysis of different databases can promote the identification of existing drugs that have the potential of administration to improve treatment in breast cancer.

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.

METHODS

Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.

RESULTS

A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.

CONCLUSION

Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.

Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases

Purpose

Retinal pigment epithelial cell autophagy dysfunction, cellular senescence, and the retinal inflammatory response are key pathogenic factors in age-related macular degeneration (AMD), which has been reviewed in our previously work in 2019. This study aims to identify genes collectively involved in these three biological processes and target drugs in AMD.

Methods

The pubmed2ensembl database was used to perform text mining. The GeneCodis database was applied to analyze gene ontology biological process and the KEGG pathway. The STRING database was used to analyze protein–protein interaction analysis and hub genes were identified by the Cytoscape software. The Drug Gene Interaction Database was used to perform drug–gene interactions.

Results

We identified 62 genes collectively involved in AMD, autophagy, cellular senescence, and inflammatory response, 19 biological processes including 42 genes, 11 enriched KEGG pathways including 37 genes, and 12 hub genes step by step via the above biomedical databases. Finally, five hub genes (IL-6, VEGF-A, TP53, IL-1β, and transforming growth factor [TGF]-β1) and their specific interaction modes were identified, corresponding with 24 target drugs with therapeutic potential for AMD.

Conclusions

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 are pivotal in autophagy, cellular senescence, and the inflammatory response in AMD, corresponding with 24 drugs with therapeutic potential for AMD, providing definite molecular mechanisms for further research and new possibilities for AMD treatment in the future.

Translational Relevance

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 may be new targets for AMD gene therapy and drug development.

Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis

Background

The aim of this study was to identify the candidate biomarkers and pathways associated with psoriasis. GSE13355 and GSE14905 were extracted from the Gene Expression Omnibus (GEO) database. Then the differentially expressed genes (DEGs) with |logFC| > 2 and adjusted P < 0.05 were chosen. In addition, the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEGs were performed. Then, the GO terms with P < 0.05 and overlap coefficient greater than 0.5 were integrated by EnrichmentMap. Additionally, risk subpathways analysis for DEGs was also conducted by using the iSubpathwayMiner package to obtain more psoriasis-related DEGs and pathways. Finally, protein-protein interaction (PPI) network analysis was performed to identify the hub genes, and the DGIdb database was utilized to search for the candidate drugs for psoriasis.

Results

A total of 127 DEGs which were mostly associated with keratinization, keratinocyte differentiation, and epidermal cell differentiation biological processes were identified. Based on these GO terms, 3 modules (human skin, epidermis and cuticle differentiation, and enzyme activity) were constructed. Moreover, 9 risk subpathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism were screened. Finally, PPI network analysis demonstrated that CXCL10 was the hub gene with the highest degree, and CXCR2, CXCL10, IVL, OASL, and ISG15 were the potential gene targets of the drugs for treating psoriasis.

Conclusion

Psoriasis may be mostly caused by keratinization, keratinocyte differentiation, and epidermal cell differentiation; the pathogeneses were more related with pathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism. Besides, some psoriasis-related genes such as SPRR genes, HSD11B1, GGH, CXCR2, IVL, OASL, ISG15, and CXCL10 may be important targets in psoriatic therapy.

COX-2 Signaling in the Tumor Microenvironment

Tumorigenesis is a multistep, complicated process, and many studies have been completed over the last few decades to elucidate this process. Increasingly, many studies have shifted focus toward the critical role of the tumor microenvironment (TME), which consists of cellular players, cell-cell communications, and extracellular matrix (ECM). In the TME, cyclooxygenase-2 (COX-2) has been found to be a key molecule mediating the microenvironment changes. COX-2 is an inducible form of the enzyme that converts arachidonic acid into the signal transduction molecules (thromboxanes and prostaglandins). COX-2 is frequently expressed in many types of cancers and has been closely linked to its occurrence, progression, and prognosis. For example, COX-2 has been shown to (1) regulate tumor cell growth, (2) promote tissue invasion and metastasis, (3) inhibit apoptosis, (4) suppress antitumor immunity, and (5) promote sustainable angiogenesis. In this chapter, we summarize recent advances of studies that have evaluated COX-2 signaling in TME.

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy

Background

Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC.

Methods

mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, and miRNA-gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC.

Results

Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC.

Conclusions

In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.

Computational Drug Discovery in Chemotherapy-induced Alopecia via Text Mining and Biomedical Databases

PURPOSE

Chemotherapy-induced alopecia (CIA) is a common and often stressful adverse effect associated with chemotherapy. CIA can cause more psychosocial pressure in patients, including effects on sexuality, self-esteem, and social relationships. We analyzed publicly available data to identify drugs formulated for topical use targeting the relevant CIA molecular pathways by using computational tools.

METHODS

The genes associated with CIA were determined by text mining, and the gene ontology of the gene set was studied using the Functional Enrichment analysis tool. Protein-protein interaction network analysis was performed using the String database. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in CIA.

FINDINGS

Our analysis identified 427 genes common to CIA text-mining concepts. Gene enrichment analysis and protein-protein interaction analysis yielded 19 genes potentially targetable by a total of 29 drugs that could possibly be formulated for topical application.

IMPLICATIONS

The findings from the present analysis would give a new thought to help discover more effective agents, and present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.

Cyclooxygenase-2 in cancer: A review

Cyclooxygenase-2 (COX-2) is frequently expressed in many types of cancers exerting a pleiotropic and multifaceted role in genesis or promotion of carcinogenesis and cancer cell resistance to chemo- and radiotherapy. COX-2 is released by cancer-associated fibroblasts (CAFs), macrophage type 2 (M2) cells, and cancer cells to the tumor microenvironment (TME). COX-2 induces cancer stem cell (CSC)-like activity, and promotes apoptotic resistance, proliferation, angiogenesis, inflammation, invasion, and metastasis of cancer cells. COX-2 mediated hypoxia within the TME along with its positive interactions with YAP1 and antiapoptotic mediators are all in favor of cancer cell resistance to chemotherapeutic drugs. COX-2 exerts most of the functions through its metabolite prostaglandin E2. In some and limited situations, COX-2 may act as an antitumor enzyme. Multiple signals are contributed to the functions of COX-2 on cancer cells or its regulation. Members of mitogen-activated protein kinase (MAPK) family, epidermal growth factor receptor (EGFR), and nuclear factor-κβ are main upstream modulators for COX-2 in cancer cells. COX-2 also has interactions with a number of hormones within the body. Inhibition of COX-2 provides a high possibility to exert therapeutic outcomes in cancer. Administration of COX-2 inhibitors in a preoperative setting could reduce the risk of metastasis in cancer patients. COX-2 inhibition also sensitizes cancer cells to treatments like radio- and chemotherapy. Chemotherapeutic agents adversely induce COX-2 activity. Therefore, choosing an appropriate chemotherapy drugs along with adjustment of the type and does for COX-2 inhibitors based on the type of cancer would be an effective adjuvant strategy for targeting cancer.

Potential Therapeutic Drugs for Parkinson's Disease Based on Data Mining and Bioinformatics Analysis

The objective is to search potential therapeutic drugs for Parkinson's disease based on data mining and bioinformatics analysis and providing new ideas for research studies on “new application of conventional drugs.” Method differential gene candidates were obtained based on data mining of genes of PD brain tissue, original gene data analysis, differential gene crossover, pathway enrichment analysis, and protein interaction, and potential therapeutic drugs for Parkinson's disease were obtained through drug-gene relationship. Result. 250 common differential genes were obtained from 3 research studies, and 31 differential gene candidates were obtained through gene enrichment analysis and protein interaction. 10 drugs such as metformin hydrochloride were directly or indirectly correlated to differential gene candidates. Conclusion. Potential therapeutic drugs that may be used for prevention and treatment of Parkinson's disease were discovered through data mining and bioinformatics analysis, which provided new ideas for research and development of drugs. Results showed that metformin hydrochloride and other drugs had certain therapeutical effect on Parkinson's disease, and melbine (DMBG) can be used for treatment of Parkinson's disease and type 2 diabetes patients.