Integrating subpathway analysis to identify candidate agents for hepatocellular carcinoma

Identifying potential biomarkers for the diagnosis and treatment of IgA nephropathy based on bioinformatics analysis

BACKGROUND

IgA nephropathy (IgAN) has become the leading cause of end-stage renal disease in young adults. Nevertheless, the current diagnosis exclusively relies on invasive renal biopsy, and specific treatment is deficient. Thus, our study aims to identify potential crucial genes, thereby providing novel biomarkers for the diagnosis and therapy of IgAN.

METHODS

Three microarray datasets were downloaded from GEO official website. Differentially expressed genes (DEGs) were identified by limma package. GO and KEGG analysis were conducted. Tissue/organ-specific DEGs were distinguished via BioGPS. GSEA was utilized to elucidate the predominant enrichment pathways. The PPI network of DEGs was established, and hub genes were mined through Cytoscape. The CTD database was employed to determine the association between hub genes and IgAN. Infiltrating immune cells and their relationship to hub genes were evaluated based on CIBERSORT. Furthermore, the diagnostic effectiveness of hub markers was subsequently predicted using the ROC curves. The CMap database was applied to investigate potential therapeutic drugs. The expression level and diagnostic accuracy of TYROBP was validated in the cell model of IgAN and different renal pathologies.

RESULTS

A total of 113 DEGs were screened, which were mostly enriched in peptidase regulator activity, regulation of cytokine production, and collagen-containing extracellular matrix. Among these DEGs, 67 genes manifested pronounced tissue and organ specificity. GSEA analysis revealed that the most significant enriched gene sets were involved in proteasome pathway. Ten hub genes (KNG1, FN1, ALB, PLG, IGF1, EGF, HRG, TYROBP, CSF1R, and ITGB2) were recognized. CTD showed a close connection between ALB, IGF, FN1 and IgAN. Immune infiltration analysis elucidated that IGF1, EGF, HRG, FN1, ITGB2, and TYROBP were closely associated with infiltrating immune cells. ROC curves reflected that all hub genes, especially TYROBP, exhibited a good diagnostic value for IgAN. Verteporfin, moxonidine, and procaine were the most significant three therapeutic drugs. Further exploration proved that TYROBP was not only highly expressed in IgAN, but exhibited high specificity for the diagnosis of IgAN.

CONCLUSIONS

This study may offer novel insights into the mechanisms involved in IgAN occurrence and progression and the selection of diagnostic markers and therapeutic targets for IgAN.

Epigenetic Alterations of DNA Methylation and miRNA Contribution to Lung Adenocarcinoma

This study focused on the epigenetic alterations of DNA methylation and miRNAs for lung adenocarcinoma (LUAD) diagnosis and treatment using bioinformatics analyses. DNA methylation data and mRNA and miRNA expression microarray data were obtained from The Cancer Genome Atlas (TCGA) database. The differentially methylated genes (DMGs), differentially expressed genes (DEGs), and differentially expressed miRNAs were analyzed by using the limma package. The DAVID database performed GO and KEGG pathway enrichment analyses. Using STRING and Cytoscape, we constructed the protein-protein interaction (PPI) network and achieved visualization. The online analysis tool CMap was used to identify potential small-molecule drugs for LUAD. In LUAD, 607 high miRNA-targeting downregulated genes and 925 low miRNA-targeting upregulated genes, as well as 284 hypermethylated low-expression genes and 315 hypomethylated high-expression genes, were obtained. They were mainly enriched in terms of pathways in cancer, neuroactive ligand-receptor interaction, cAMP signaling pathway, and cytosolic DNA-sensing pathway. In addition, 40 upregulated and 84 downregulated genes were regulated by both aberrant alternations of DNA methylation and miRNAs. Five small-molecule drugs were identified as a potential treatment for LUAD, and five hub genes (SLC2A1, PAX6, LEP, KLF4, and FGF10) were found in PPI, and two of them (SLC2A1 and KLF4) may be related to the prognosis of LUAD. In summary, our study identified a series of differentially expressed genes associated with epigenetic alterations of DNA methylation and miRNA in LUAD. Five small-molecule drugs and five hub genes may be promising drugs and targets for LUAD treatment.

Downexpression of HSD17B6 correlates with clinical prognosis and tumor immune infiltrates in hepatocellular carcinoma

Background

Hydroxysteroid 17-Beta Dehydrogenase 6 (HSD17B6), a key protein involved in synthetizing dihydrotestosterone, is abundant in the liver. Previous studies have suggested a role for dihydrotestosterone in modulating progress of various malignancies, and HSD17B6 dysfunction was associated with lung cancer and prostate cancer. However, little is known about the detailed role of HSD17B6 in hepatocellular carcinoma (HCC).

Methods

Clinical implication and survival data related to HSD17B6 expression in patients with HCC were obtained through TCGA, ICGC, ONCOMINE, GEO and HPA databases. Survival analysis plots were drawn with Kaplan–Meier Plotter. The ChIP-seq data were obtained from Cistrome DB. Protein–Protein Interaction and gene functional enrichment analyses were performed in STRING database. The correlations between HSD17B6 and tumor immune infiltrates was investigated via TIMER and xCell. The proliferation, migration and invasion of liver cancer cells transfected with HSD17B6 were evaluated by the CCK8 assay, wound healing test and transwell assay respectively. Expression of HSD17B6, TGFB1 and PD-L1 were assessed by quantitative RT-PCR.

Results

HSD17B6 expression was lower in HCC compared to normal liver and correlated with tumor stage and grade. Lower expression of HSD17B6 was associated with worse OS, PFS, RFS and DSS in HCC patients. HNF4A bound to enhancer and promoter regions of HSD17B6 gene, activating its transcription, and DNA methylation of HSD17B6 promoter negatively controlled the expression. HSD17B6 and its interaction partners were involved in androgen metabolism and biosynthesis in liver. HSD17B6 inhibited tumor cell proliferation, migration and invasion in liver cancer cells and low expression of HSD17B6 correlated with high immune cells infiltration, relative reduction of immune responses and multiple immune checkpoint genes expression in HCC, probably by regulating the expression of TGFB1.

Conclusions

This study indicate that HSD17B6 could be a new biomarker for the prognosis of HCC and an important negative regulator of immune responses in HCC.

Application of Big Data analysis in gastrointestinal research

Big Data, which are characterized by certain unique traits like volume, velocity and value, have revolutionized the research of multiple fields including medicine. Big Data in health care are defined as large datasets that are collected routinely or automatically, and stored electronically. With the rapidly expanding volume of health data collection, it is envisioned that the Big Data approach can improve not only individual health, but also the performance of health care systems. The application of Big Data analysis in the field of gastroenterology and hepatology research has also opened new research approaches. While it retains most of the advantages and avoids some of the disadvantages of traditional observational studies (case-control and prospective cohort studies), it allows for phenomapping of disease heterogeneity, enhancement of drug safety, as well as development of precision medicine, prediction models and personalized treatment. Unlike randomized controlled trials, it reflects the real-world situation and studies patients who are often under-represented in randomized controlled trials. However, residual and/or unmeasured confounding remains a major concern, which requires meticulous study design and various statistical adjustment methods. Other potential drawbacks include data validity, missing data, incomplete data capture due to the unavailability of diagnosis codes for certain clinical situations, and individual privacy. With continuous technological advances, some of the current limitations with Big Data may be further minimized. This review will illustrate the use of Big Data research on gastrointestinal and liver diseases using recently published examples.

In silico analysis identified miRNA‑based therapeutic agents against glioblastoma multiforme

MicroRNAs (miRNAs or miRs) contribute to the development of various malignant neoplasms, including glioblastoma multiforme (GBM). The present study aimed to explore the pathogenesis of GBM and to identify latent therapeutic agents for patients with GBM, based on an in silico analysis. Gene chips that provide miRNA expression profiling in GBM were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) were also determined via the RobustRankAggreg algorithm. The target genes of DEMs were predicted and then intersected with GBM‑associated genes that were collected from the Gene Expression Profiling Interactive Analysis. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the overlapping genes were then performed. Simultaneously, a connectivity map (CMap) analysis was performed to screen for potential therapeutic agents for GBM. A total of 10 DEMs (hsa‑miR‑196a, hsa‑miR‑10b, hsa‑miR‑196b, hsa‑miR‑18b, hsa‑miR‑542‑3p, hsa‑miR‑129‑3p, hsa‑miR‑1224‑5p, hsa‑miR‑876‑3p and hsa‑miR‑770‑5p) were obtained from three GEO gene chips (GSE25631, GSE42657 and GSE61710). Then, 1,720 target genes of the 10 miRNAs and 4,185 differently expressed genes in GBM were collected. By intersecting the aforementioned gene clusters, the present study identified 390 overlapping genes. GO and KEGG analyses of the 390 genes demonstrated that these genes were involved in certain cancer‑associated biological functions and pathways. Eight genes [(GTPase NRas (NRAS), calcium/calmodulin‑dependent protein kinase type II subunit Gamma (CAMK2G), platelet‑derived growth factor receptor alpha (PDGFRA), calmodulin 3 (CALM3), cyclin‑dependent kinase 6 (CDK6), calcium/calmodulin‑dependent protein kinase type II subunit beta (CAMK2B), retinoblastoma‑associated protein (RB1) and protein kinase C beta type (PRKCB)] that were centralized in the glioma pathway were selected for CMap analysis. Three chemicals (W‑13, gefitinib and exemestane) were identified as putative therapeutic agents for GBM. In summary, the present study identified three miRNA‑based chemicals for use as a therapy for GBM. However, more experimental data are needed to verify the therapeutic properties of these latent drugs in GBM.

Drug repositioning in head and neck squamous cell carcinoma: An integrated pathway analysis based on connectivity map and differential gene expression

The severe damage to health and social burden caused by head and neck squamous cell carcinoma (HNSCC) generated an urgent need to develop novel anti-cancer therapy. Currently, drug repositioning has risen in responses to the proper time as an efficient approach to invention of new anti-cancer therapies. In the present study, we aimed to screen candidate drugs for HNSCC by integrating HNSCC-related pathways from differentially expressed genes (DEGs) and drug-affected pathways from connectivity map (CMAP). We also endeavored to unveil the molecular mechanism of HNSCC through creating drug-target network and protein-to-protein (PPI) network of component DEGs in key overlapping pathways. As a result, a total of 401 DEGs were obtained from TCGA and GTEx mRNA-seq data. Taking the intersection part of 27 HNSCC-related Kyoto Encyclopedia of Genes and Genomes pathways and 33 drug-affected pathways, we retained 22 candidate drugs corresponding to two key pathways (cell cycle and p53 signaling pathways) of the five overlapping pathways. Two of the hub genes (PCNA and CCND1) identified from the PPI network of component DEGs in cell cycle and p53 signaling pathways were defined as the critical targets of candidate drugs with increased protein expression in HNSCC tissues, which was reported by the human protein atlas (HPA) database and cBioPortal. Finally, we validated via molecular docking analysis that two drugs with unknown effects in HNSCC: MG-262 and bepridil might perturb the development of HNSCC through targeting PCNA. These candidate drugs possessed broad application prospect as medication for HNSCC.

The underlying molecular mechanism and potential drugs for treatment in papillary renal cell carcinoma: A study based on TCGA and Cmap datasets

Papillary renal cell carcinoma (PRCC) accounts for 15–20% of all kidney neoplasms and continually attracts attention due to the increase in the incidents in which it occurs. The molecular mechanism of PRCC remains unclear and the efficacy of drugs that treat PRCC lacks sufficient evidence in clinical trials. Therefore, it is necessary to investigate the underlying mechanism in the development of PRCC and identify additional potential anti-PRCC drugs for its treatment. The differently expressed genes (DEGs) of PRCC were identified, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional annotation. Then, potential drugs for PRCC treatment were predicted by Connectivity Map (Cmap) based on DEGs. Furthermore, the latent function of query drugs in PRCC was explored by integrating drug-target, drug-pathway and drug-protein interactions. In total, 627 genes were screened as DEGs, and these DEGs were annotated using KEGG pathway analyses and were clearly associated with the complement and coagulation cascades, amongst others. Then, 60 candidate drugs, as predicted based on DEGs, were obtained from the Cmap database. Vorinostat was considered as the most promising drug for detailed discussion. Following protein-protein interaction (PPI) analysis and molecular docking, vorinostat was observed to interact with C3 and ANXN1 proteins, which are the upregulated hub genes and may serve as oncologic therapeutic targets in PRCC. Among the top 20 metabolic pathways, several significant pathways, such as complement and coagulation cascades and cell adhesion molecules, may greatly contribute to the development and progression of PRCC. Following the performance of the PPI network and molecular docking tests, vorinostat exhibited a considerable and promising application in PRCC treatment by targeting C3 and ANXN1.

Observational Study of PD-L1, TGF-β, and Immune Cell Infiltrates in Hepatocellular Carcinoma

Introduction: Hepatocellular carcinoma (HCC) typically develops in cirrhotic livers, with increased programed death ligand 1 (PD-L1) and transforming growth factor beta (TGF-β) activity implicated in immunosuppression.

Methods: In an observational study of HCC liver samples, we determined the incidence of PD-L1 and immune cell (IC) infiltrates, and signs of TGF-β activity. HCCs were characterized by the incidence and distribution of PD-L1⁺ cells, and CD8⁺, CD68⁺, and FoxP3⁺ infiltrating ICs in HCC and surrounding liver. Gene expression signatures (GESs) associated with TGF-β activity and ICs were evaluated by RNAseq.

Results: In non-neoplastic cirrhotic and non-cirrhotic liver, PD-L1 occurred on sinusoidal lining cells (mostly Kupffer cells), endothelial cells and ICs. In HCC, PD-L1⁺ tumor cells were rare. Most PD-L1⁺ cells were identified as ICs. CD8⁺, CD68⁺, and FoxP3⁺ ICs were associated with HCC, particularly in the invasive margin. CD8⁺ cell incidence correlated with PD-L1⁺ cells, consistent with PD-L1 being upregulated in response to pre-existing cytotoxic T-lymphocyte activity. TGFB1 mRNA levels and TGF-β activation GES correlated with the strength of the tumor-associated macrophage GES.

Conclusion: Inhibition of PD-L1⁺ ICs and TGF-β activity and their respective immunomodulatory pathways may contribute to antitumor effects in HCC.

Identification of putative drugs for gastric adenocarcinoma utilizing differentially expressed genes and connectivity map

Gastric adenocarcinoma (GAC) is a challenging disease with dim prognosis even after surgery; hence, novel treatments for GAC are in urgent need. The aim of the present study was to explore new potential compounds interfering with the key pathways related to GAC progression. The differentially expressed genes (DEGs) between GAC and adjacent tissues were identified from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Connectivity Map (CMap) was performed to screen candidate compounds for treating GAC. Subsequently, pathways affected by compounds were overlapped with those enriched by the DEGs to further identify compounds which had anti-GAC potential. A total of 843 DEGs of GAC were identified. Via Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, 13 pathways were significantly enriched. Moreover, 78 compounds with markedly negative correlations with DEGs were revealed in CMap database (P<0.05 and Enrichment <0). Subpathways of cell cycle and p53 signaling pathways, and core genes of these compounds, cyclin B1 (CCNB1) and CDC6, were identified. This study further revealed seven compounds that may be effective against GAC; in particular methylbenzethonium chloride and alexidine have never yet been reported for GAC treatment. In brief, the candidate drugs identified in this study may provide new options to improve the treatment of patients with GAC. However, the biological effects of these drugs need further investigation.

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

Patients with esophageal carcinoma (ESCA) have a poor prognosis and high mortality rate. Although standard therapies have had effect, there is an urgent requirement to develop novel options, as increasing drug tolerance has been identified in clinical practice. In the present study, differentially expressed genes (DEGs) of ESCA were identified in The Cancer Genome Atlas and Genotype-Tissue Expression databases. Functional and protein-protein interaction (PPI) analyses were performed. The Connectivity Map (CMAP) was selected to predict drugs for the treatment of ESCA, and their target genes were acquired from the Search Tool for Interactions of Chemicals (STITCH) by uploading the Simplified Molecular-Input Line-Entry System structure. Additionally, significant target genes and ESCA-associated hub genes were extracted using another PPI analysis, and the corresponding drugs were added to construct a network. Furthermore, the binding affinity between predicted drug candidates and ESCA-associated hub genes was calculated using molecular docking. Finally, 827 DEGs (|log₂ fold-change|≥2; q-value <0.05), which are principally involved in protein digestion and absorption (P<0.005), the plasminogen-activating cascade (P<0.01), as well as the ‘biological regulation’ of the Biological Process, ‘membrane’ of the Cellular Component and ‘protein binding’ of the Molecular Function categories, were obtained. Additionally, 11 hub genes were obtained from the PPI network (all degrees ≥30). Furthermore, the 15 first screen drugs were extracted from CMAP (score <−0.85) and the 9 second screen drugs with 70 target genes were extracted from STITCH. Furthermore, another PPI analysis extracted 51 genes, and apigenin, baclofen, Prestwick-685, menadione, butyl hydroxybenzoate, gliclazide and valproate were selected as drug candidates for ESCA. Those molecular docking results with a docking score of >5.52 indicated the significance of apigenin, Prestwick-685 and menadione. The results of the present study may lead to novel drug candidates for ESCA, among which Prestwick-685 and menadione were identified to be significant new drug candidates.

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein-protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.

Repurposing old drugs in oncology: Opportunities with clinical and regulatory challenges ahead

WHAT IS KNOWN AND OBJECTIVE

In order to expedite the availability of drugs to treat cancers in a cost-effective manner, repurposing of old drugs for oncological indications is gathering momentum. Revolutionary advances in pharmacology and genomics have demonstrated many old drugs to have activity at novel antioncogenic pharmacological targets. We decided to investigate whether prospective studies support the promises of nonclinical and retrospective clinical studies on repurposing three old drugs, namely metformin, valproate and astemizole.

METHODS

We conducted an extensive literature search through PubMed to gather representative nonclinical and retrospective clinical studies that investigated the potential repurposing of these three drugs for oncological indications. We then searched for prospective studies aimed at confirming the promises of retrospective data.

RESULTS AND DISCUSSION

While evidence from nonclinical and retrospective clinical studies with these drugs appears highly promising, large scale prospective studies are either lacking or have failed to substantiate this promise. We provide a brief discussion of some of the challenges in repurposing. Principal challenges and obstacles relate to heterogeneity of cancers studied without considering their molecular signatures, trials with small sample size and short duration, failure consider issues of ethnicity of study population and effective antioncogenic doses of the drug studied.

WHAT IS NEW AND CONCLUSION

Well-designed prospective studies demonstrating efficacy are required for repurposing old drugs for oncology indications, just as they are for new chemical entities for any indication. Early and ongoing interactions with regulatory authorities are invaluable. We outline a tentative framework for a structured approach to repurposing old drugs for novel indications in oncology.

Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma

Background

This study compared tumor‐related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma (LUAD) treatment.

Methods

Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses were performed based on LUAD differentially expressed genes from The Cancer Genome Atlas (TCGA) project and genotype‐tissue expression controls. These results were compared to various known compounds using the Connectivity Mapping dataset. The clinical significance of the hub genes identified by overlapping pathway enrichment analysis was further investigated using data mining from multiple sources. A drug‐pathway network for LUAD was constructed, and molecular docking was carried out.

Results

After the integration of 57 LUAD‐related pathways and 35 pathways affected by small molecules, five overlapping pathways were revealed. Among these five pathways, the p53 signaling pathway was the most significant, with CCNB1, CCNB2, CDK1, CDKN2A, and CHEK1 being identified as hub genes. The p53 signaling pathway is implicated as a risk factor for LUAD tumorigenesis and survival. A total of 88 molecules significantly inhibiting the five LUAD‐related oncogenic pathways were involved in the LUAD drug‐pathway network. Daunorubicin, mycophenolic acid, and pyrvinium could potentially target the hub gene CHEK1 directly.

Conclusion

Our study highlights the critical pathways that should be targeted in the search for potential LUAD treatments, most importantly, the p53 signaling pathway. Some compounds, such as ciclopirox and AG‐028671, may have potential roles for LUAD treatment but require further experimental verification.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.