Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer

Integration of miRNA expression analysis of purified leukocytes and whole blood reveals blood-borne candidate biomarkers for lung cancer

Changes in leukocyte populations may confound the disease-associated miRNA signals in the blood of cancer patients. We aimed to develop a method to detect differentially expressed miRNAs from lung cancer whole blood samples that are not influenced by variations in leukocyte proportions. The Ref-miREO method identifies differential miRNAs unaffected by changes in leukocyte populations by comparing the within-sample relative expression orderings (REOs) of miRNAs from healthy leukocyte subtypes and those from lung cancer blood samples. Over 77% of the differential miRNAs observed between lung cancer and healthy blood samples overlapped with those between myeloid-derived and lymphoid-derived leukocytes, suggesting the potential impact of changes in leukocyte populations on miRNA profile. Ref-miREO identified 16 differential miRNAs that target 19 lung adenocarcinoma-related genes previously linked to leukocytes. These miRNAs showed enrichment in cancer-related pathways and demonstrated high potential as diagnostic biomarkers, with the LASSO regression models effectively distinguishing between healthy and lung cancer blood or serum samples (all AUC > 0.85). Additionally, 12 of these miRNAs exhibited significant prognostic correlations. The Ref-miREO method offers valuable candidates for circulating biomarker detection in cancer that are not affected by changes in leukocyte populations.

Caffeic acid, a dietary polyphenol, pre-sensitizes pancreatic ductal adenocarcinoma to chemotherapeutic drug

Resistance to chemotherapeutics is an eminent cause that leads to search for options that help in diminution of pancreatic ductal adenocarcinoma (PDAC) by overcoming resistance issues. Caffeic acid (CFA), a polyphenol occurring in many dietary foods, is known to show antidiabetic and anticancer properties potential. To unveil the effect of CFA on PDAC, we carried out this research in PDAC cells, following which we checked the combination effect of CFA and chemotherapeutics and pre-sensitization effects of CFA. Multitudinous web-based approaches were applied for identifying CFA targets in PDAC and then getting their interconnections. Subsequently, we manifested CFA effects by in-vitro analysis showing IC50 concentrations of 37.37 and 15.06 µM on Panc-1 and Mia-PaCa-2, respectively. The combination index of CFA with different drugs was explored which showed the antagonistic effects of combination treatment leading to further investigation of the pre-sensitizing effects. CFA pre-sensitization reduced IC50 concentration of doxorubicin in both PDAC cell lines which also triggered ROS generation determined by 2',7'-dichlorofluorescin diacetate assay. The differential gene expression analysis after CFA treatment showed discrete genes affected in both cells, i.e. N-Cad and Cas9 in Panc-1 and Pi3K/AkT/mTOR along with p53 in Mia-PaCa-2. Collectively, this study investigated the role of CFA as PDAC therapeutics and explored the mechanism in mitigating resistance of PDAC by sensitizing to chemotherapeutics.Communicated by Ramaswamy H. Sarma.

Changes in DNA Methylation and mRNA Expression in Lung Tissue after Long-Term Supplementation with an Increased Dose of Cholecalciferol

Maintaining an appropriate concentration of vitamin D is essential for the proper functioning of the body, regardless of age. Nowadays, there are more and more indications that vitamin D supplementation at higher than standard doses may show protective and therapeutic effects. Our study identified differences in the body's response to long-term supplementation with cholecalciferol at an increased dose. Two groups of pigs were used in the experiment. The first group received a standard dose of cholecalciferol (grower, 2000 IU/kg feed, and finisher, 1500 IU/kg feed), and the second group received an increased dose (grower, 3000 IU/kg feed, and finisher, 2500 IU/kg feed). After slaughter, lung samples were collected and used for RRBS and mRNA sequencing. Analysis of the methylation results showed that 2349 CpG sites had significantly altered methylation patterns and 1116 (47.51%) identified DMSs (Differentially Methylated Sites) were related to genes and their regulatory sites. The mRNA sequencing results showed a significant change in the expression of 195 genes. The integrated analysis identified eleven genes with DNA methylation and mRNA expression differences between the analyzed groups. The results of this study suggested that an increased vitamin D intake may be helpful for the prevention of lung cancer and pulmonary fibrosis. These actions may stem from the influence of vitamin D on the expression of genes associated with collagen production, such as SHMT1, UGT1A6, and ITIH2.The anti-cancer properties of vitamin D are also supported by changes in KLHL3 and TTPA gene expression.

Pharmacoinformatic screening of phytoconstituent and evaluation of its anti-PDAC effect using in vitro studies

With no prominent treatment for pancreatic ductal adenocarcinoma (PDAC) in conventional chemotherapy, recent studies have focused on uniting conventional and traditional medicines including plant phytoconstituents. Herein, we used pharmacoinformatic studies to identify potent phytoconstituent as ligand having inhibition activities against canonical anticancer targets, and evaluated its effect on PDAC cell lines. SwissTargetPrediction and SuperPred tools were utilized to segregate protein targets of ligand in humans, following which FunRich was applied to garner its targets in PDAC. STRING analysis predicted protein-protein interactions and dynamic simulation studies confirmed stability of ligand-protein complex. For in vitro cytotoxic potential, ligand treatment at different concentrations was given to PDAC cell lines both alone and combined with gemcitabine, followed by evaluation of effects on migration. Differential gene expression was checked using PCR for evaluating mechanism of cytotoxicity. Results showed pentagalloylglucose (PGG) with highest docking and MMGBSA scores for Cyclooxygenase 2 (Cox2) inhibition site. SwissTargetPrediction and SuperPred analysis detected 40 targets of PGG in PDAC. Simulation data showed stability of protein-ligand complex. In in vitro experiments Mia-PaCa-2 was more sensitive to PGG than Panc-1. PGG successfully inhibited migration both alone and in combination with gemcitabine. Additionally, PGG treatment induced apoptosis in both the cell lines; but showed antagonism when combined with gemcitabine. In conclusion, our report demonstrates PGG has good binding with Cox2 and showed anti-PDAC activity by inhibiting migration and inducing apoptosis, thus it can be used as a therapy option. But further studies are required to confirm its behaviour as a combination therapy drug.Communicated by Ramaswamy H. Sarma.

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.

Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.

Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.

Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

Identification of a novel circRNA-miRNA-mRNA regulatory axis in hepatocellular carcinoma based on bioinformatics analysis

In recent years, circular RNAs (circRNAs) have been found to play an essential regulatory role in hepatocellular carcinoma (HCC) through various mechanisms, particularly the endogenous competitive RNA (ceRNA) mechanism. Therefore, it is significant to explore the circRNAs in hepatoma. In this study, we constructed the ceRNA and survival network using Cytoscape. We also used R, Perl software, and multiple online databases and platforms, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), to perform overall survival, immune cell infiltration, immune checkpoints, pathway activity, and anticancer drug sensitivity analysis of the genes. Finally, the receiver operator characteristic curve (ROC) analysis was performed to identify the diagnosis value of the genes. KEGG analysis revealed the T cell receptor signaling pathway as the main enrichment pathway. A total of 29 genes related to survival and prognosis were screened out. The findings suggest that ZNF544, WDR76, ACTG1, RASSF3, E2F3, ASRGL1, and POGK are associated with multilevel immune cell infiltration. Additionally, immune checkpoint analysis screened out the ACTG1, E2F3, RASSF3, and WDR76. It was also revealed that the WDR76, E2F3, ASRGL1, and POGK mainly activated the cell cycle and DNA damage response (DDR) pathway. The results suggest that the sensitivity toward trametinib, refametinib (RDEA119), and selumetinib correlates to the expression of WDR76. ROC analysis showed that the area under the curve (AUC) of all genes in the regulatory axis was greater than 0.7. The identified hsa_circ_0000417/hsa_circ_0002688/hsa_circ_0001387--hsa-miR-199a-5p--WDR76 regulatory axis may provide new insights into the progression, clinical diagnosis, and treatment of HCC.

LINC02323 facilitates development of lung squamous cell carcinoma by miRNA sponge and RBP dysregulation and links to poor prognosis

BACKGROUND

The poor outcome of patients with lung squamous cell carcinoma (LUSC) highlights the importance of the identification of novel effective prognostic markers and therapeutic targets. Long noncoding RNAs (lncRNAs) have generally been considered to serve important roles in tumorigenesis and the development of various types of cancer, including LUSC.

METHODS

Here, we aimed to investigate the role of LINC02323 in LUSC and its potential mechanisms by performing comprehensive bioinformatic analyses.

RESULTS

LINC02323 was elevated and positively associated with unfavorable prognosis of LUSC patients. LINC02323 exerted oncogenic function by competitively binding to miR-1343-3p and miR-6783-3p, thereby upregulating L1CAM expression. Indeed, we also determined that LINC02323 could interact with the RNA-binding protein DDX3X, which regulates various stages of RNA expression and processing.

CONCLUSION

Taken together, we identified that LINC02323 and its indirect target L1CAM can act as novel biomarkers for determining the prognosis of patients with LUSC and thus deserves further study.

Bioinformatic Analysis Identifies of Potential miRNA-mRNA Regulatory Networks Involved in the Pathogenesis of Lung Cancer

Objective. The purpose of the present study was to explore the biomarkers related to lung cancer based on the bioinformatics method, which might be new targets for lung cancer treatment. Methods. GSE17681 and GSE18842 were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) in lung cancer samples were screened via the GEO2R online tool. DEMs were submitted to the mirDIP website to predict target genes. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted via uploading DEGs to the DAVID database. The protein-protein interaction network (PPI) of the DEGs was analyzed by STRING’s online tool. Then, the PPI network was visualized using Cytoscape 3.8.0. Results. 46 DEMs were identified in GSE17681, and the website predicted that there were 873 target genes of these DEMs. 1029 DEGs were identified in the GSE18842 chip. GO analysis suggested that the co-DEGs participated in the canonical Wnt signaling pathway, regulation of the Wnt signaling pathway, a serine/threonine kinase signaling pathway, the Wnt signaling pathway, and cell-cell signaling by Wnt. KEGG analysis results showed the co-DEGs of GSE17681 and GSE18842 were related to the Hippo signaling pathway and adhesion molecules. In addition, six hub genes that were related to lung cancer were identified as hub genes, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1. Conclusions. The present study identified six hub genes that were related to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1, which might be a potential target for lung cancer.

Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma

Background

The coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.

Methods

Two sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.

Results

We discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.

Conclusion

We identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.