Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates

Specific gut microbiome signature predicts hepatitis B virus-related hepatocellular carcinoma patients with microvascular invasion

BACKGROUND

We aimed to assess differences in intestinal microflora between patients with operable hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) and those without MVI. Additionally, we investigated the potential of the microbiome as a non-invasive biomarker for patients with MVI.

METHODS

We analyzed the preoperative gut microbiomes (GMs) of two groups, the MVI (n = 46) and non-MVI (n = 56) groups, using 16S ribosomal RNA gene sequencing data. At the operational taxonomic unit level, we employed random forest models to predict MVI risk and validated the results in independent validation cohorts [MVI group (n = 17) and non-MVI group (n = 15)].

RESULTS

β diversity analysis, utilizing weighted UniFrac distances, revealed a significant difference between the MVI and non-MVI groups, as indicated by non-metric multidimensional scaling and principal coordinate analysis. We also observed a significant correlation between the characteristic intestinal microbial communities at the genus level and their main functions. Nine optimal microbial markers were identified, with an area under the curve of 79.76% between 46 MVI and 56 non-MVI samples and 79.80% in the independent verification group.

CONCLUSION

This pioneering analysis of the GM in patients with operable HBV-HCC with and without MVI opens new avenues for treating HBV-HCC with MVI. We successfully established a diagnostic model and independently verified microbial markers for patients with MVI. As preoperative targeted biomarkers, GM holds potential as a non-invasive tool for patients with HBV-HCC with MVI.

Molecular signature of neutrophil extracellular trap mediating disease module in idiopathic inflammatory myopathy

The rarity and heterogeneity of idiopathic inflammatory myopathy (IIM) pose challenges for researching IIM in affected individuals. We analyzed integrated transcriptomic datasets obtained using muscle tissues from patients with five distinct IIM subtypes to investigate the shared and distinctive cellular and molecular characteristics. A transcriptomic dataset of muscle tissues from normal controls (n = 105) and patients with dermatomyositis (n = 89), polymyositis (n = 33), inclusion body myositis (n = 121), immune-mediated necrotizing myositis (n = 75), and anti-synthetase syndrome (n = 18) was used for differential gene-expression analysis, functional-enrichment analysis, gene set-enrichment analysis, disease-module identification, and kernel-based diffusion scoring. Damage-associated molecular pattern-associated pathways and neutrophil-mediated immunity were significantly enriched across different IIM subtypes, although their activities varied. Interferons-signaling pathways were differentially activated across all five IIM subtypes. In particular, neutrophil extracellular trap (NET) formation was significantly activated and correlated with Fcγ R-mediated signaling pathways. NET formation-associated genes were key for establishing disease modules, and FCGRs, C1QA, and SERPINE1 markedly perturbed the disease modules. Integrated transcriptomic analysis of muscle tissues identified NETs as key components of neutrophil-mediated immunity involved in the pathogenesis of IIM subtypes and, thus, has therapeutically targetable value.

Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients.

METHODS

Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins.

FINDINGS

We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments.

INTERPRETATION

We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

A comparative study of COVID-19 transcriptional signatures between clinical samples and preclinical cell models in the search for disease master regulators and drug repositioning candidates

Coronavirus disease 2019 (COVID-19) is an acute viral disease with millions of cases worldwide. Although the number of daily new cases and deaths has been dropping, there is still a need for therapeutic alternatives to deal with severe cases. A promising strategy to prospect new therapeutic candidates is to investigate the regulatory mechanisms involved in COVID-19 progression using integrated transcriptomics approaches. In this work, we aimed to identify COVID-19 Master Regulators (MRs) using a series of publicly available gene expression datasets of lung tissue from patients which developed the severe form of the disease. We were able to identify a set of six potential COVID-19 MRs related to its severe form, namely TAL1, TEAD4, EPAS1, ATOH8, ERG, and ARNTL2. In addition, using the Connectivity Map drug repositioning approach, we identified 52 different drugs which could be used to revert the disease signature, thus being candidates for the design of novel clinical treatments. Furthermore, we compared the identified signature and drugs with the ones obtained from the analysis of nasopharyngeal swab samples from infected patients and preclinical cell models. This comparison showed significant similarities between them, although also revealing some limitations on the overlap between clinical and preclinical data in COVID-19, highlighting the need for careful selection of the best model for each disease stage.

Necroptosis Identifies Novel Molecular Phenotypes and Influences Tumor Immune Microenvironment of Lung Adenocarcinoma

This study aims to investigate the immune and epigenetic mutational landscape of necroptosis in lung adenocarcinoma (LUAD), identify novel molecular phenotypes, and develop a prognostic scoring system based on necroptosis regulatory molecules for a better understanding of the tumor immune microenvironment (TIME) in LUAD. Based on the Cancer Genome Atlas and Gene Expression Omnibus database, a total of 29 overlapped necroptosis-related genes were enrolled to classify patients into different necroptosis phenotypes using unsupervised consensus clustering. We systematically correlated the phenotypes with clinical features, immunocyte infiltrating levels, and epigenetic mutation characteristics. A novel scoring system was then constructed, termed NecroScore, to quantify necroptosis of LUAD by principal component analysis. Three distinct necroptosis phenotypes were confirmed. Two clusters with high expression of necroptosis-related regulators were “hot tumors”, while another phenotype with low expression was a “cold tumor”. Molecular characteristics, including mutational frequency and types, copy number variation, and regulon activity differed significantly among the subtypes. The NecroScore, as an independent prognostic factor (HR=1.086, 95%CI=1.040-1.133, p<0.001), was able to predict the survival outcomes and show that patients with higher scores experienced a poorer prognosis. It could also evaluate the responses to immunotherapy and chemotherapeutic efficiency.

In conclusion, necroptosis-related molecules are correlated with genome diversity in pan-cancer, playing a significant role in forming the TIME of LUAD. Necroptosis phenotypes can distinguish different TIME and molecular features, and the NecroScore is a promising biomarker for predicting prognosis, as well as immuno- and chemotherapeutic benefits in LUAD.

Bioinformatics Analysis of mRNAs and miRNAs for Identifying Potential Biomarkers in Lung Adenosquamous Carcinoma

Background

Lung adenosquamous carcinoma (LASC) is a special type of lung cancer. LASC is a malignant tumor with strong aggressiveness and a poor prognosis. Previous studies have revealed that microRNAs (miRNAs) are widely involved in the development of tumors by targeting mRNA. This study is aimed at identifying the key mRNAs and miRNAs of LASC and constructing miRNA-mRNA networks for deeply comprehending the latent molecular mechanisms.

Methods

mRNA dataset (GSE51852) and miRNA dataset (GSE51853) were extracted and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were picked out by the GEO2R web tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted in the DAVID database. The protein-protein interaction (PPI) network was performed and analyzed by using the STRING database and Cytoscape software, respectively. TransmiR v2.0 was applied to predict potential transcription factors of miRNAs. The target genes of DEMs were predicted in the miRWalk database.

Results

In comparison to normal tissues, a total of 1458 DEGs (511 upregulated and 947 downregulated) and 13 DEMs (5 upregulated and 8 downregulated) were screened out in LASC tissues. The PPI network of the DEGs displayed five key modules and seventeen hub genes. Six target genes of the DEMs were predicted, and five essential miRNA-mRNA regulatory pairs were established. Ensuingly, CENPF, one of the target genes, was also the hub genes of GSE51852, which was obtained from MCODE and cytoHubba and regulated by hsa-miR-205.

Conclusions

We constructed the miRNA-mRNA regulatory pairs, which are helpful to study the potential regulatory mechanisms and find out promising diagnosis biomarkers and therapeutic targets for LASC.

Whole-Genome Methylation Study of Congenital Lung Malformations in Children

Background and Objectives

The treatment of asymptomatic patients with congenital pulmonary malformations (CPMs) remains controversial, partially because the relationship between congenital lung malformations and malignancy is still undefined. Change in methylation pattern is a crucial event in human cancer, including lung cancer. We therefore studied all differentially methylated regions (DMRs) in a series of CPMs in an attempt to find methylation anomalies in genes already described in association with malignancy.

Methods

The DNA extracted from resected congenital lung malformations and control lung tissue was screened using Illumina MethylationEPIC arrays. Comparisons between the group of malformed samples or the malformed samples of same histology or each malformed sample and the controls and between a pleuropulmonary blastoma (PPB) and controls were performed. Moreover, each malformed sample was pairwise compared with its respective control. All differentially methylated regions (DMRs) with an adjusted p-value <0,05 were studied.

Results

Every comparison highlighted a number of DMRs closed to genes involved either in cell proliferation or in embryonic development or included in the Cancer Gene Census. Their abnormal methylation had been already described in lung tumors.

Conclusions

Methylation anomalies already described in lung tumors and also shared by the PPB were found in congenital lung malformations, regardless the histology. The presence of methylation abnormalities is suggestive of a correlation between congenital lung malformations and some step of malignant transformation.

Transcriptional Regulation of Cancer Immune Checkpoints: Emerging Strategies for Immunotherapy

The study of immune evasion has gained a well-deserved eminence in cancer research by successfully developing a new class of therapeutics, immune checkpoint inhibitors, such as pembrolizumab and nivolumab, anti-PD-1 antibodies. By aiming at the immune checkpoint blockade (ICB), these new therapeutics have advanced cancer treatment with notable increases in overall survival and tumor remission. However, recent reports reveal that 40-60% of patients fail to benefit from ICB therapy due to acquired resistance or tumor relapse. This resistance may stem from increased expression of co-inhibitory immune checkpoints or alterations in the tumor microenvironment that promotes immune suppression. Because these mechanisms are poorly elucidated, the transcription factors that regulate immune checkpoints, known as "master regulators", have garnered interest. These include AP-1, IRF-1, MYC, and STAT3, which are known to regulate PD/PD-L1 and CTLA-4. Identifying these and other potential master regulators as putative therapeutic targets or biomarkers can be facilitated by mining cancer literature, public datasets, and cancer genomics resources. In this review, we describe recent advances in master regulator identification and characterization of the mechanisms underlying immune checkpoints regulation, and discuss how these master regulators of immune checkpoint molecular expression can be targeted as a form of auxiliary therapeutic strategy to complement traditional immunotherapy.

Parkinson's Disease Master Regulators on Substantia Nigra and Frontal Cortex and Their Use for Drug Repositioning

Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases. Available evidences support the view of PD as a complex disease, being the outcome of interactions between genetic and environmental factors. In face of diagnosis and therapy challenges, and the elusive PD etiology, the use of alternative methodological approaches for the elucidation of the disease pathophysiological mechanisms and proposal of novel potential therapeutic interventions has become increasingly necessary. In the present study, we first reconstructed the transcriptional regulatory networks (TN), centered on transcription factors (TF), of two brain regions affected in PD, the substantia nigra pars compacta (SNc) and the frontal cortex (FCtx). Then, we used case-control studies data from these regions to identify TFs working as master regulators (MR) of the disease, based on region-specific TNs. Twenty-nine regulatory units enriched with differentially expressed genes were identified for the SNc, and twenty for the FCtx, all of which were considered MR candidates for PD. Three consensus MR candidates were found for SNc and FCtx, namely ATF2, SLC30A9, and ZFP69B. In order to search for novel potential therapeutic interventions, we used these consensus MR candidate signatures as input to the Connectivity Map (CMap), a computational drug repositioning webtool. This analysis resulted in the identification of four drugs that reverse the expression pattern of all three MR consensus simultaneously, benperidol, harmaline, tubocurarine chloride, and vorinostat, thus suggested as novel potential PD therapeutic interventions.

Detection and Independent Validation of Model-Based Quantitative Transcriptional Regulation Relationships Altered in Lung Cancers

Differential expressions of genes are widely evaluated for the diagnosis and prognosis correlations with diseases. But limited studies investigate how transcriptional regulations are quantitatively altered in diseases. This study proposes a novel model-based quantitative measurement of transcriptional regulatory relationships between mRNA genes and Transcription Factor (TF) genes (mqTrans features). This study didn't consider the regulatory relationships between TF genes, so the mRNA genes were the protein-coding genes excluding the TF genes. The models are trained in the control samples in a lung cancer dataset and evaluated in two independent datasets and the hold-out testing samples from the third dataset. Twenty-nine mRNA genes are detected with transcriptional regulations quantitatively altered in lung cancers. The transcriptional modification technologies like RNA interference (RNAi) may be utilized to restore the altered transcriptional regulations in lung cancers.

Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates

BACKGROUND

Lung adenocarcinoma is the major cause of cancer-related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment.

METHODS

We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case-control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning.

RESULTS

The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival.

CONCLUSION

Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.