Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection

Pathogenesis and clinical features of severe hepatitis E virus infection

The hepatitis E virus (HEV), a member of the Hepeviridae family, is a small, non-enveloped icosahedral virus divided into eight distinct genotypes (HEV-1 to HEV-8). Only genotypes 1 to 4 are known to cause diseases in humans. Genotypes 1 and 2 commonly spread via fecal-oral transmission, often through the consumption of contaminated water. Genotypes 3 and 4 are known to infect pigs, deer, and wild boars, often transferring to humans through inadequately cooked meat. Acute hepatitis caused by HEV in healthy individuals is mostly asymptomatic or associated with minor symptoms, such as jaundice. However, in immunosuppressed individuals, the disease can progress to chronic hepatitis and even escalate to cirrhosis. For pregnant women, an HEV infection can cause fulminant liver failure, with a potential mortality rate of 25%. Mortality rates also rise amongst cirrhotic patients when they contract an acute HEV infection, which can even trigger acute-on-chronic liver failure if layered onto pre-existing chronic liver disease. As the prevalence of HEV infection continues to rise worldwide, highlighting the particular risks associated with severe HEV infection is of major medical interest. This text offers a brief summary of the characteristics of hepatitis developed by patient groups at an elevated risk of severe HEV infection.

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

Screening of miRNAs as prognostic biomarkers and their associated hub targets across Hepatocellular carcinoma using survival-based bioinformatics approach

BACKGROUND

The hepatocellular carcinoma (HCC) incident rate is gradually increasing yearly despite all the research and efforts taken by scientific communities and governing bodies. Approximately 90% of all liver cancer cases belong to HCC. Usually, HCC patients approach the treatment in the late stages of this malignancy which becomes the primary cause of high mortality rate. The knowledge about molecular pathogenesis of HCC is limited and needs more attention from researchers to identify the driver genes and miRNAs, which causes to translate this information into clinical practice. Therefore, the key regulators identification of miRNA-mRNA regulatory network is essential to identify HCC-associated genes.

METHODOLOGY

We extracted microRNA (miRNA) and messenger RNA (mRNA) expression datasets of normal and tumor HCC patient samples from UCSC Xena followed by identifying differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Univariate and multivariate cox-proportional hazard models were utilized to identify DEMs having significant association with overall survival (OS). Kaplan-Meier (KM) plotter was used to validate the presence of prognostic DEMs. A risk-score model was used to evaluate the effectiveness of KM-plotter validated DEMs combination on risk of samples. Target DEGs of prognostic miRNAs were identified via sources such as miRTargetLink and miRWalk followed by their validation in an external microarray cohort and enrichment analysis.

RESULTS

562 DEGs and 388 DEMs were identified followed by seven prognostic miRNAs (i.e., miR-19a, miR-19b, miR-30d-5p, miR-424-5p, miR-3677-5p, miR-3913-5p, miR-7705) post univariate, multivariate, risk-score model evaluation and KM-plotter analyses. ANLN, MRO, CPEB3 were their targets and were also validated in GSE84005 dataset.

CONCLUSIONS

The findings of this study decipher that most significant miRNAs and their identified target genes have association with apoptosis, inflammation, cell cycle regulation and cancer-related pathways, which appear to contribute to HCC pathogenesis and therefore, the discovery of new targets.

Potential role of microRNAs in personalized medicine against hepatitis: a futuristic approach

MicroRNAs (miRNAs) have been the subject of extensive research for many years, primarily in the context of diseases such as cancer. However, our appreciation of their significance in viral infections, particularly in hepatitis, has increased due to the discovery of their association with both the host and the virus. Hepatitis is a major global health concern and can be caused by various viruses, including hepatitis A to E. This review highlights the key factors associated with miRNAs and their involvement in infections with various viruses that cause hepatitis. The review not only emphasizes the expression profiles of miRNAs in hepatitis but also puts a spotlight on their potential for diagnostics and therapeutic interventions. Ongoing extensive studies are propelling the therapeutic application of miRNAs, addressing both current limitations and potential strategies for the future of miRNAs in personalized medicine. Here, we discuss the potential of miRNAs to influence future medical research and an attempt to provide a thorough understanding of their diverse roles in hepatitis and beyond.

Integrative multiomics and weighted network approach reveals the prognostic role of RPS7 in lung squamous cell carcinoma pathogenesis

Lung cancer is one of the most commonly occurring malignant cancers with the highest rate of mortality globally. Difference between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) and their treatment strategies according to genetic markers may be helpful in reducing the cancer progression and increasing the overall survival (OS) in patients. LUSC is known for comparatively less typical onco-drivers, target therapy resistance, marked genomic complexity, and a reasonably higher mutation rate. The mRNA-seq data and clinical information of LUAD and LUSC cohorts from UCSC Xena comprising 437 and 379 patient samples were extracted. Differential expression and weighted network analyses revealed 47 and 18 hub differentially expressed genes (DEGs) corresponding to LUAD and LUSC cohorts. These hub DEGs were further subjected to protein-protein interaction network (PPIN) and OS analyses. Lower mRNA expression levels of both RPS15A and RPS7 worsened the OS of LUSC patients. Additionally, both these prognostic biomarkers were validated via external sources such as UALCAN, cBioPortal, TIMER, and HPA. RPS7 had higher mutation frequency compared to RPS15A and showed significant negative correlations with infiltrating levels of CD4+ T cells, CD8+ T cells, neutrophils, and macrophages. Our findings provided novel insights into biomarker discovery and the critical role of ribosomal biogenesis especially smaller ribosomal subunit in pathogenesis of LUSC.

Identification of interferon-stimulated genes with modulated expression during hepatitis E virus infection in pig liver tissues and human HepaRG cells

Introduction

Hepatitis E virus (HEV) is a common cause of enterically transmitted acute hepatitis worldwide. The virus is transmitted by the fecal-oral route via the consumption of contaminated water supplies and is also a zoonotic foodborne pathogen. Swine are the main reservoir of zoonotic HEV. In humans, HEV infection is usually asymptomatic or causes acute hepatitis that is self-limited. However, fulminant hepatic failure and chronic cases of HEV infection can occur in some patients. In contrast, HEV infection in pigs remains asymptomatic, although the virus replicates efficiently, suggesting that swine are able to control the virus pathogenesis. Upon viral infection, IFN is secreted and activates cellular pathways leading to the expression of many IFN-stimulated genes (ISGs). ISGs can restrict the replication of specific viruses and establish an antiviral state within infected and neighboring cells.

Methods

In this study, we used PCR arrays to determine the expression level of up to 168 ISGs and other IFN-related genes in the liver tissues of pigs infected with zoonotic HEV-3c and HEV-3f and in human bipotent liver HepaRG cells persistently infected with HEV-3f.

Results and discussion

The expression of 12 and 25 ISGs was found to be up-regulated in infected swine livers and HepaRG cells, respectively. The expression of CXCL10, IFIT2, MX2, OASL and OAS2 was up-regulated in both species. Increased expression of IFI16 mRNA was also found in swine liver tissues. This study contributes to the identification of potential ISGs that could play a role in the control or persistence of HEV infection.

Investigating the role of Kinesin family in lung adenocarcinoma via integrated bioinformatics approach

Lung cancer is the leading cause of mortality from cancer worldwide. Lung adenocarcinoma (LUAD) is a type of non-small cell lung cancer (NSCLC) with highest prevalence. Kinesins a class of motor proteins are shown to be involved in carcinogenesis. We conducted expression, stage plot and survival analyses on kinesin superfamily (KIF) and scrutinized the key prognostic kinesins. Genomic alterations of these kinesins were studied thereafter via cBioPortal. A protein-protein interaction network (PPIN) of selected kinesins and 50 closest altering genes was constructed followed by gene ontology (GO) term and pathway enrichment analyses. Multivariate survival analysis based on CpG methylation of selected kinesins was performed. Lastly, we conducted tumor immune infiltration analysis. Our results found KIF11/15/18B/20A/2C/4A/C1 to be significantly upregulated and correlated with poor survival in LUAD patients. These genes also showed to be highly associated with cell cycle. Out of our seven selected kinesins, KIFC1 showed the highest genomic alteration with highest number of CpG methylation. Also, CpG island (CGI) cg24827036 was discovered to be linked to LUAD prognosis. Therefore, we deduced that reducing the expression of KIFC1 could be a feasible treatment strategy and that it can be a wonderful individual prognostic biomarker. CGI cg24827036 can also be used as a therapy site in addition to being a great prognostic biomarker.

Breast Cancer Prognostic Hub Genes Identified by Integrated Transcriptomic and Weighted Network Analysis: A Road Toward Personalized Medicine

Breast cancer (BC) is the second-most common type and among the leading causes of worldwide cancer-related deaths. There is marked person-to-person variability in susceptibility to, and phenotypic expression and prognosis of BC, a predicament that calls for personalized medicine and individually tailored therapeutics. In this study, we report new observations on prognostic hub genes and key pathways involved in BC. We used the data set GSE109169, comprising 25 pairs of BC and adjacent normal tissues. Using a high-throughput transcriptomic approach, we selected data on 293 differentially expressed genes to establish a weighted gene coexpression network. We identified three age-linked modules where the light-gray module strongly correlated with BC. Based on the gene significance and module membership features, peptidase inhibitor 15 (PI15) and KRT5 were identified as our hub genes from the light-gray module. These genes were further verified at transcriptional and translational levels across 25 pairs of BC and adjacent normal tissues. Their promoter methylation profiles were assessed based on various clinical parameters. In addition, these hub genes were used for Kaplan-Meier survival analysis, and their correlation with tumor-infiltrating immune cells was investigated. We found that PI15 and KRT5 may be potential biomarkers and potential drug targets. These findings call for future research in a larger sample size, which could inform diagnosis and clinical management of BC, thus paving the way toward personalized medicine.