In silico analysis of differential gene expressions in biliary stricture and hepatic carcinoma

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.

METHODS

Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.

RESULTS

A total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.

CONCLUSIONS

We constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell-cell communication in the tumor microenvironment.

In silico identification and functional prediction of differentially expressed genes in South Asian populations associated with type 2 diabetes

Type 2 diabetes (T2D) is one of the major metabolic disorders in humans caused by hyperglycemia and insulin resistance syndrome. Although significant genetic effects on T2D pathogenesis are experimentally proved, the molecular mechanism of T2D in South Asian Populations (SAPs) is still limited. Hence, the current research analyzed two Gene Expression Omnibus (GEO) and 17 Genome-Wide Association Studies (GWAS) datasets associated with T2D in SAP to identify DEGs (differentially expressed genes). The identified DEGs were further analyzed to explore the molecular mechanism of T2D pathogenesis following a series of bioinformatics approaches. Following PPI (Protein-Protein Interaction), 867 potential DEGs and nine hub genes were identified that might play significant roles in T2D pathogenesis. Interestingly, CTNNB1 and RUNX2 hub genes were found to be unique for T2D pathogenesis in SAPs. Then, the GO (Gene Ontology) showed the potential biological, molecular, and cellular functions of the DEGs. The target genes also interacted with different pathways of T2D pathogenesis. In fact, 118 genes (including HNF1A and TCF7L2 hub genes) were directly associated with T2D pathogenesis. Indeed, eight key miRNAs among 2582 significantly interacted with the target genes. Even 64 genes were downregulated by 367 FDA-approved drugs. Interestingly, 11 genes showed a wide range (9-43) of drug specificity. Hence, the identified DEGs may guide to elucidate the molecular mechanism of T2D pathogenesis in SAPs. Therefore, integrating the research findings of the potential roles of DEGs and candidate drug-mediated downregulation of marker genes, future drugs or treatments could be developed to treat T2D in SAPs.

Next-generation development and application of codon model in evolution

To date, numerous nucleotide, amino acid, and codon substitution models have been developed to estimate the evolutionary history of any sequence/organism in a more comprehensive way. Out of these three, the codon substitution model is the most powerful. These models have been utilized extensively to detect selective pressure on a protein, codon usage bias, ancestral reconstruction and phylogenetic reconstruction. However, due to more computational demanding, in comparison to nucleotide and amino acid substitution models, only a few studies have employed the codon substitution model to understand the heterogeneity of the evolutionary process in a genome-scale analysis. Hence, there is always a question of how to develop more robust but less computationally demanding codon substitution models to get more accurate results. In this review article, the authors attempted to understand the basis of the development of different types of codon-substitution models and how this information can be utilized to develop more robust but less computationally demanding codon substitution models. The codon substitution model enables to detect selection regime under which any gene or gene region is evolving, codon usage bias in any organism or tissue-specific region and phylogenetic relationship between different lineages more accurately than nucleotide and amino acid substitution models. Thus, in the near future, these codon models can be utilized in the field of conservation, breeding and medicine.

Screening potential biomarkers of cholangiocarcinoma based on gene chip meta-analysis and small-sample experimental research

Cholangiocarcinoma (CCA) is a rare malignant tumor associated with poor prognosis. This study aimed to identify CCA biomarkers by investigating differentially expressed genes (DEGs) between CCA patients and healthy subjects obtained from the Gene Expression Omnibus database. Bioinformatics tools, including the Illumina BaseSpace Correlation Engine (BSCE) and Gene Expression Profiling Interactive Analysis (GEPIA), were used. The initial DEGs from GSE26566, GSE31370, and GSE77984 were analyzed using GEO2R and Venn, and protein–protein interaction networks were constructed using STRING. The BSCE was applied to assess curated CCA studies to select additional DEGs and them DEGs across the 10 biosets, which was supported by findings in the literature. The final 18 DEGs with clinical significance for CCA were further verified using GEPIA. These included CEACAM6, EPCAM, LAMC2, MMP11, KRT7, KRT17, KRT19, SFN, and SOX9, which were upregulated, and ADH1A, ALDOB, AOX1, CTH, FGA, FGB, FGG, GSTA1, and OTC, which were downregulated in CCA patients. Among these 18 genes, 56 groups of genes (two in each group) were significantly related, and none were independently and differentially expressed. The hub genes FGA, OTC, CTH, and MMP11, which were most correlated with the 18 DEGs, were screened using STRING. The significantly low expression of FGA, OTC, and CTH and significantly high expression of MMP11 were verified by immunohistochemical analysis. Overall, four CCA biomarkers were identified that might regulate the occurrence and development of this disease and affect the patient survival rate, and they have the potential to become diagnostic and therapeutic targets for patients with CCA.

ADH1C inhibits progression of colorectal cancer through the ADH1C/PHGDH /PSAT1/serine metabolic pathway

Colorectal cancer (CRC) is the third most common cancer in men and the second most common cancer in women worldwide. CRC is the second leading cause of cancer-related deaths. Although some progress in the treatment of CRC has been achieved, the molecular mechanism of CRC is still unclear. In this study, alcohol dehydrogenase 1C(ADH1C) was first identified as a target gene closely associated with the development of CRC by the comprehensive application of transcriptomics, proteomics, metabonomics and in silico analysis. The ADH1C mRNA and protein expression in CRC cell lines and tumor tissues was lower than that in normal intestinal epithelial cell lines and healthy tissues. Overexpression of ADH1C inhibited the growth, migration, invasion and colony formation of CRC cell lines and prevented the growth of xenograft tumors in nude mice. The inhibitory effects of ADH1C on CRC cells in vitro were exerted by reducing the expression of PHGDH/PSAT1 and the serine level. This inhibition could be partially reversed by adding serine to the culture medium. These results showed that ADH1C is a potential drug target in CRC.

In silico functional and pathway analysis of risk genes and SNPs for type 2 diabetes in Asian population

Type 2 diabetes (T2D) has earned widespread recognition as a primary cause of death, disability, and increasing healthcare costs. There is compelling evidence that hereditary factors contribute to the development of T2D. Clinical trials in T2D have mostly focused on genes and single nucleotide polymorphisms (SNPs) in protein-coding areas. Recently, it was revealed that SNPs located in noncoding areas also play a significant impact on disease vulnerability. It is required for cell type-specific gene expression. However, the precise mechanism by which T2D risk genes and SNPs work remains unknown. We integrated risk genes and SNPs from genome-wide association studies (GWASs) and performed comprehensive bioinformatics analyses to further investigate the functional significance of these genes and SNPs. We identified four intriguing transcription factors (TFs) associated with T2D. The analysis revealed that the SNPs are engaged in chromatin interaction regulation and/or may have an effect on TF binding affinity. The Gene Ontology (GO) study revealed high enrichment in a number of well-characterized signaling pathways and regulatory processes, including the STAT3 and JAK signaling pathways, which are both involved in T2D metabolism. Additionally, a detailed KEGG pathway analysis identified two major T2D genes and their prospective therapeutic targets. Our findings underscored the potential functional significance of T2D risk genes and SNPs, which may provide unique insights into the disease’s pathophysiology.

MicroRNA-29c Acting on FOS Plays a Significant Role in Nonalcoholic Steatohepatitis Through the Interleukin-17 Signaling Pathway

Nonalcoholic fatty liver disease is the most common hepatic disease in western countries and is even more ubiquitous in Asian countries. Our study determined that TH17/Treg cells were imbalanced in animal models. Based on our interest in the mechanism underlying TH17/Treg cell imbalance in nonalcoholic fatty liver mice, we conducted a joint bioinformatics analysis to further investigate this process. Common gene sequencing analysis was based on one trial from one sequencing platform, where gene expression analysis and enrichment analysis were the only analyses performed. We compared different sequencing results from different trials performed using different sequencing platforms, and we utilized the intersection of these analytical results to perform joint analysis. We used a bioinformatics analysis method to perform enrichment analysis and map interaction network analysis and predict potential microRNA sites. Animal experiments were also designed to validate the results of the data analysis based on quantitative polymerase chain reaction (qPCR) and western blotting. Our results revealed 8 coexisting differentially expressed genes (DEGs) and 7 hinge genes. The identified DEGs may influence nonalcoholic steatosis hepatitis through the interleukin-17 pathway. We found that microRNA-29c interacts with FOS and IGFBP1. Polymerase chain reaction analyses revealed both FOS and microRNA-29c expression in NASH mice, and western blot analyses indicated the same trend with regard to FOS protein levels. Based on these results, we suggest that microRNA-29c acts on FOS via the interleukin-17 signaling pathway to regulate TH17/Treg cells in NASH patients.

Neuroblastoma: An Updated Review on Biology and Treatment

BACKGROUND

Neuroblastoma (NB) is the second leading extracranial solid tumors of early childhood and clinically characterized by the presence of round, small, monomorphic cells with excess nuclear pigmentation (hyperchromasia).Owing to a lack of definitive treatment against NB and less survival rate in high-risk patients, there is an urgent requirement to understand molecular mechanisms associated with NB in a better way, which in turn can be utilized for developing drugs towards the treatment of NB in human.

OBJECTIVES

In this review, an approach was adopted to understand major risk factors, pathophysiology, the molecular mechanism associated with NB, and various therapeutic agents that can serve as drugs towards the treatment of NB in humans.

CONCLUSION

Numerous genetic (e.g., MYCN amplification), perinatal, and gestational factors are responsible for developing NB. However, no definite environmental or parental exposures responsible for causing NB have been confirmed to date. Though intensive multimodal treatment approaches, namely, chemotherapy, surgery & radiation, may help in improving the survival rate in children, these approaches have several side effects and do not work efficiently in high-risk patients. However, recent studies suggested that numerous phytochemicals, namely, vincristine, and matrine have a minimal side effect in the human body and may serve as a therapeutic drug during the treatment of NB. Most of these phytochemicals work in a dose-dependent manner and hence must be prescribed very cautiously. The information discussed in the present review will be useful in the drug discovery process as well as treatment and prevention on NB in humans.

Curcumin - A Novel Therapeutic Agent in the Prevention of Colorectal Cancer

Background:

Colorectal cancer is the third important cause of cancer-associated deaths across the world. Hence, there is an urgent need for understanding the complete mechanism associated with colorectal cancer, which in turn can be utilized toward early detection as well as the treatment of colorectal cancer in humans. Though colorectal cancer is a complex process and chemotherapy is the first step toward the treatment of colorectal cancer, recently several studies suggested that dietary phytochemicals may also aid significantly in reducing colorectal cancer risk in human. However, only few phytochemicals, specifically curcumin derived from the rhizomes of Curcuma longa, have better chemotherapeutic property, which might be because of its ability to regulate the activity of key factors associated with the initiation, promotion, as well as progression of tumors.

Objectives:

In the present review, the authors made an attempt to summarize the physiochemical properties of curcumin, which in turn prevent colorectal cancer via regulating numerous cell signaling as well as genetic pathways.

Conclusions:

Accumulated evidence suggested that curcumin suppresses tumour/colon cancer in various ways, (a) restricting cell cycle progression, or stimulating apoptosis, (b) restricting angiogenesis, anti-apoptotic proteins expression, cell survival signaling pathways & their cross-communication and (c) regulating immune responses. The information discussed in the present review will be useful in the drug discovery process as well as the treatment and prevention of colorectal cancer in humans.

Deciphering crucial genes in coeliac disease by bioinformatics analysis

Coeliac disease (CD) is a chronic autoimmune disease that is characterized by malabsorption in sensitive individuals. CD is triggered by the ingestion of grains containing gluten. CD is concomitant with several other disorders, including dermatitis herpetiformis, selective IgA deficiency, thyroid disorders, diabetes mellitus, various connective tissue disorders, inflammatory bowel disease, and rheumatoid arthritis. The advent of high throughput technologies has provided a massive wealth of data which are processed in various omics scale fields. These approaches have revolutionized the medical research and monitoring of the biological systems. In this regard, omics scaled analyses of CD by Comparative Toxicogenomics Database (CTD), DISEASES, and GeneCards databases have retrieved 2656 CD associated genes. Amongst, 54 genes were assigned by Venn Diagram of the intersection to be shared by these 3 databases for CD. These common genes were subjected to further analysis and screening. The Enrich database, GeneMANIA, Cytoscape, and WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) were employed for functional analysis. These analyses indicated that the obtained genes are mainly involved in the immune system and signalling pathways related to autoimmune diseases. The STAT1, ALB, IL10, IL2, IL4, IL17A, TGFB1, IL1B, IL6, TNF, IFNG hub genes were particularly indicated to have significant roles in CD. Functional analyses of these hub genes by GeneMANIA indicated that they are involved in immune systems regulation. Moreover, 25 out of 54 genes were identified to be seed genes by the WebGestalt database. Gene set analysis with GEO2R tool from Gene Expression Omnibus (GEO) showed that there were 15 significant genes in GSE76168, 29 significant genes in GSE87460, 12 significant genes in GSE87458, 9 significant genes in GSE87457, 3753 significant genes in GSE112102 and 1043 significant genes in GSE102991 with differential expression in coeliac patients compared to controls. The IRF1and STAT1 genes were common between the significant genes from GEO and the 54 CD related genes from three public databases. In the light these results, nine key genes, including IRF1, STAT1, IL17A, TGFB1, ALB, IL10, IL2, IL4, and IL1B, were identified to be associated with CD. These findings could be used to find novel diagnostic biomarkers, understand the pathology of disease, and devise more efficient treatments.

miR-99b-3p promotes hepatocellular carcinoma metastasis and proliferation by targeting protocadherin 19

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world, with characteristics of high morbidity and mortality. Identifying clinically practical targets and uncovering the potential mechanism for HCC were urgent for us. Though aberrantly expressed miR-99b-3p has been reported in several cancers, the expression and roles of miR-99b-3p in HCC remain uncovered. In the present study, we demonstrated for the first time that miR-99b-3p was overexpressed in HCC by our findings and data from GEO datasets. Statistical analysis revealed that highly expressed miR-99b-3p was closely related to malignant clinicopathological characteristics and poorer prognosis of HCC patients. Furthermore, results from Wound healing assay, Transwell assays, 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)assay and 5-ethynyl-2'-deoxyuridine (EdU) assay revealed that miR-99b-3p played a promoting role in migration, invasion and proliferation of HCC cells. Then, by bioinformatics tools, luciferase reporter gene assay, integrative database analysis, and Pearson correlation analysis and so on, protocadherin 19 (PCDH19) was identified as the target of miR-99b-3p in HCC cells. Furthermore, rescue experiments were conducted to confirm the mediator role of PCDH19 for miR-99b-3p. Collectively, we demonstrate that miR-99b-3p promotes HCC metastasis and proliferation by directly targeting PCDH19. MiR-99b-3p may become a potential therapy target for HCC.

Computational approach to understand molecular mechanism involved in BPH resistance in Bt- rice plant

In silico approach was utilised to identify differentially expressed key hub genes during BPH infestation on Bt rice plant, under laboratory conditions. Re-analysis of GSE74745 data with in-house R scripts and STRING database reveals that only 5 key hub genes, namely Os05g0176100, Os06g0683200, Os07g0208500, Os07g0252400 and Os07g0424400, belonging to cellulose synthase family, are differentially expressed and have confidence score ≥0.9 among themselves. Conserve domain analysis of all proteins encoded via these 5 key hub genes reveals that they have a common cellulose synthase domain, in which "Plant-Conserved Region" (PCR) is highly conserved. After binding with other domains of cellulose synthase proteins or other accessory proteins, like sucrose synthase, PCR serves as a metabolic channel to deliver UDP-Glucose, which is the main substrate for cellulose synthesis, into the active site of cellulose synthase and initiate cellulose synthesis. Simulation study of recently solved topological model of PCR [PDB ID: 5JNP] and molecular docking studies of PCR with UDP-glucose reveals that, during BPH infestation, in nearby phloem tissue where BPH suck sap, there is an increase interaction of UDP-glucose with PCR and other accessory proteins which in turn increases both the stability of PCR and the production of cellulose, finally causing callose deposition at that site and hence causing longer nymphal developmental period and lower fertility of BPH infested on Bt rice. In near future, these differentially identified 5 hub genes could be possible targets for controlling BPH infestation in rice plant under field conditions and increasing rice yield globally.

Identification and characterization of differentially expressed genes in Type 2 Diabetes using in silico approach

Diabetes mellitus is clinically characterized by hyperglycemia. Though many studies have been done to understand the mechanism of Type 2 Diabetes (T2D), however, the complete network of diabetes and its associated disorders through polygenic involvement is still under debate. The present study designed to re-analyze publicly available T2D related microarray raw datasets present in GEO database and T2D genes information present in GWAS catalog for screening out differentially expressed genes (DEGs) and identify key hub genes associated with T2D. T2D related microarray data downloaded from Gene Expression Omnibus (GEO) database and re-analysis performed with in house R packages scripts for background correction, normalization and identification of DEGs in T2D. Also retrieved T2D related DEGs information from GWAS catalog. Both DEGs lists were grouped after removal of overlapping genes. These screened DEGs were utilized further for identification and characterization of key hub genes in T2D and its associated diseases using STRING, WebGestalt and Panther databases. Computational analysis reveal that out of 99 identified key hub gene candidates from 348 DEGs, only four genes (CCL2, ELMO1, VEGFA and TCF7L2) along with FOS playing key role in causing T2D and its associated disorders, like nephropathy, neuropathy, rheumatoid arthritis and cancer via p53 or Wnt signaling pathways. MIR-29, and MAZ_Q6 are identified potential target microRNA and TF along with probable drugs alprostadil, collagenase and dinoprostone for the key hub gene candidates. The results suggest that identified key DEGs may play promising roles in prevention of diabetes.

Network-based meta-analysis in the identification of biomarkers for papillary thyroid cancer

Papillary thyroid carcinoma (PTC) has been increasing across the world with incomplete understanding of its pathogenesis. We aimed to investigate gene alterations and biomarkers contributing to PTC development. A total of five eligible microarray datasets including 94 PTC and 81 normal thyroid samples were included to identify gene expression signatures. Using integrative meta-analysis of expression data (INMEX) program, we identified a total of 2699 differentially expressed genes (DEGs) (1333 overexpressed and 1366 underexpressed genes) in PTC relative to normal thyroid samples. The top 100 upregualted and downregulated DEGs identified in the meta-analysis were further validated in The Cancer Genome Atlas (TCGA) dataset for PTC with high consistency. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed pathways in cancer, proteoglycans in cancer, focal adhesion, axon guidance, and ECM-receptor interaction among the top 5 most enriched pathways. Network-based meta-analysis identified FN1 and TRAF6 to be the most highly ranked hub genes among the overexpressed and underexpressed genes, respectively, both of which are involved in pathways in cancer. The most enriched terms for Gene Ontology (GO) of biological processes, cellular component, and molecular function were signal transduction, cytoplasm, and protein binding, respectively. Our meta-analysis comprehensively investigated DEGs, hub genes, enriched pathways and GO terms for PTC, which might provide additional approaches to explore the molecular mechanisms underlying the pathophysiology of PTC, and identify biomarkers and therapeutic targets toward PTC.