Identification of Unique Transcriptomic Signatures and Hub Genes Through RNA Sequencing and Integrated WGCNA and PPI Network Analysis in Nonerosive Reflux Disease

Role of mechanoregulation in mast cell-mediated immune inflammation of the smooth muscle in the pathophysiology of esophageal motility disorders

Major esophageal disorders involve obstructive transport of bolus to the stomach, causing symptoms of dysphagia and impaired clearing of the refluxed gastric contents. These may occur due to mechanical constriction of the esophageal lumen or loss of relaxation associated with deglutitive inhibition, as in achalasia-like disorders. Recently, immune inflammation has been identified as an important cause of esophageal strictures and the loss of inhibitory neurotransmission. These disorders are also associated with smooth muscle hypertrophy and hypercontractility, whose cause is unknown. This review investigated immune inflammation in the causation of smooth muscle changes in obstructive esophageal bolus transport. Findings suggest that smooth muscle hypertrophy occurs above the obstruction and is due to mechanical stress on the smooth muscles. The mechanostressed smooth muscles release cytokines and other molecules that may recruit and microlocalize mast cells to smooth muscle bundles, so that their products may have a close bidirectional effect on each other. Acting in a paracrine fashion, the inflammatory cytokines induce genetic and epigenetic changes in the smooth muscles, leading to smooth muscle hypercontractility, hypertrophy, and impaired relaxation. These changes may worsen difficulty in the esophageal transport. Immune processes differ in the first phase of obstructive bolus transport, and the second phase of muscle hypertrophy and hypercontractility. Moreover, changes in the type of mechanical stress may change immune response and effect on smooth muscles. Understanding immune signaling in causes of obstructive bolus transport, type of mechanical stress, and associated smooth muscle changes may help pathophysiology-based prevention and targeted treatment of esophageal motility disorders.NEW & NOTEWORTHY Esophageal disorders such as esophageal stricture or achalasia, and diffuse esophageal spasm are associated with smooth muscle hypertrophy and hypercontractility, above the obstruction, yet the cause of such changes is unknown. This review suggests that smooth muscle obstructive disorders may cause mechanical stress on smooth muscle, which then secretes chemicals that recruit, microlocalize, and activate mast cells to initiate immune inflammation, producing functional and structural changes in smooth muscles. Understanding the immune signaling in these changes may help pathophysiology-based prevention and targeted treatment of esophageal motility disorders.

Relationship between the expression of PD-L1 and 18F-FDG uptake in pancreatic ductal adenocarcinoma

BACKGROUND

PD-L1 promotes glycolysis in tumour cells. We observed a correlation between high PD-L1 expression and high 18F-FDG uptake in patients with pancreatic ductal adenocarcinoma (PDAC) in a previous study. This study aims to determine the usefulness of 18F-FDG PET/CT for evaluating the PD-L1 status in PDAC and to elucidate its rationality by integrated analyses.

METHODS

For bioinformatics analysis, WGCNA, GSEA and TIMER were applied to analyse the pathways and hub genes associated with PD-L1 and glucose uptake. 18F-FDG uptake assay was used to determine the glucose uptake rate of PDAC cells in vitro. Related genes expression were verified by RT-PCR and western blot. A retrospective analysis was performed on 47 patients with PDAC who had undergone 18F-FDG PET/CT. Maximum standardised uptake values (SUVmax) were determined. The usefulness of SUVmax for evaluating PD-L1 status was determined by receiver operating characteristic (ROC) curve analysis.

RESULTS

Bioinformatics analysis showed that several signalling pathways are associated with both PD-L1 expression and tumour glucose uptake, among which JAK-STAT may be an important one. By in vitro experiments, the regulatory role of PD-L1 on glucose uptake was demonstrated, and its dependency on the JAK-STAT pathway was also verified by the rescue study. The SUVmax of PD-L1-positive patients was significantly higher than PD-L1-negative in tumour cells (TCs) (6.1 ± 2.3 vs. 11.1 ± 4.2; P < 0.001), and in tumour-infiltrating immune cells (TIICs) (6.4 ± 3.2 vs. 8.4 ± 3.5; P < 0.001). In a multivariate analysis, SUVmax was significantly associated with PD-L1 expression in TCs and TIICs (P < 0.001 and P = 0.018, respectively). Using SUVmax cut-off values of 8.15 and 7.75, PD-L1 status in TCs and TIICs could be predicted with accuracies of 91.5% and 74.5%, respectively.

CONCLUSION

Higher 18F-FDG uptake by PDAC is associated with elevated PD-L1 expression. JAK-STAT is an important pathway that mediates PD-L1 to promote glucose uptake in PDAC.

Integrated analysis of bulk and single-cell RNA-seq reveals the role of MYC signaling in lung adenocarcinoma

MYC is one of the well-known oncogenes, and its important role in cancer still remains largely unknown. We obtained lung adenocarcinoma (LUAD) multi-omics data including genome, transcriptome, and single-cell sequencing data from multiple cohorts. We calculated the GSVA score of the MYC target v1 using the ssGSEA method, and obtained the genes highly correlated with this score by Spearman correlation analysis. Subsequent hierarchical clustering divided these genes into two gene sets highly associated with MYC signaling (S1 and S2). Unsupervised clustering based on these genes divided the LUAD samples into two distinct subgroups, namely, the MYC signaling inhibition group (C1) and activation group (C2). The MCP counter package in R was used to assess tumor immune cell infiltration abundance and ssGSEA was used to calculate gene set scores. The scRNA-seq was used to verify the association of MYC signaling to cell differentiation. We observed significant differences in prognosis, clinical characteristics, immune microenvironment, and genomic alterations between MYC signaling inhibition and MYC signaling activation groups. MYC-signaling is associated with genomic instability and can mediate the immunosuppressive microenvironment and promote cell proliferation, tumor stemness. Moreover, MYC-signaling activation is also subject to complex post-transcriptional regulation and is highly associated with cell differentiation. In conclusion, MYC signaling is closely related to the genomic instability, genetic alteration and regulation, the immune microenvironment landscape, cell differentiation, and disease survival in LUAD. The findings of this study provide a valuable reference to revealing the mechanism of cancer-promoting action of MYC in LUAD.

Identification of unique transcriptomic signatures and key genes through RNA sequencing and integrated WGCNA and PPI network analysis in HIV infected lung cancer

With the widespread use of highly active antiretroviral therapy (HARRT), the survival time of AIDS patients has been greatly extended. However, the incidence of lung cancer in HIV-infected patients is increasing and has become a major problem threatening the survival of AIDS patients. The aim of this study is to use Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene analysis to find possible key genes involved in HIV-infected lung cancer. In this study, using lung tissue samples from five pairs of HIV-infected lung cancer patients, second-generation sequencing was performed and transcriptomic data were obtained. A total of 132 HIV-infected lung cancer-related genes were screened out by WGCNA and differential gene expression analysis methods. Based on gene annotation analysis, these genes were mainly enriched in mitosis-related functions and pathways. In addition, in protein-protein interaction (PPI) analysis, a total of 39 hub genes were identified. Among them, five genes (ASPM, CDCA8, CENPF, CEP55, and PLK1) were present in both three hub gene lists (intersection gene, DEGs, and WCGNA module) suggesting that these five genes may become key genes involved in HIV-infected lung cancer.

Comprehensive Genetic Analysis of Tuberculosis and Identification of Candidate Biomarkers

Purpose: The purpose of this study is to use the data in the GEO database to analyze, screen biomarkers that can diagnose tuberculosis, and verification of candidate biomarkers.

Materials and methods: GSE158767 dataset were used to process WGCNA analysis, differential gene analysis, Gene ontology and KEGG analysis, protein-protein network analysis and hub genes analysis. Based on our previous study, the intersect between WGCNA and differential gene analysis could be used as candidate biomarkers. Then, the enzyme-linked immunosorbent assay was used to validate candidate biomarkers, and receiver operating characteristic was used to assess diagnose ability of candidate biomarkers.

Results: A total of 412 differential genes were screened. And we obtained 105 overlapping genes between DEGs and WGCNA. GO and KEGG analysis showed that most of the differential genes were significantly enriched in innate immunity. A total of 15 hub genes were screened, and four of them were verified by Enzyme-linked immunosorbent assay. CCL5 performed well in distinguishing the healthy group from the TB group (AUC = 0.723). And CCL19 performed well in distinguishing the TB group from the ORD groups (AUC = 0.811).

Conclusion: CCL19, C1Qb, CCL5 and HLA-DMB may play important role in tuberculosis, which indicated four genes may become effective biomarkers and could be conveniently used to facilitate the individual tuberculosis diagnosis in Chinese people.

Construction of a Redox-Related Prognostic Model with Predictive Value in Survival and Therapeutic Response for Patients with Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) represents the most common histological subtype of lung cancer. Redox plays a significant role in oncogenesis and antitumor immunity. In this study, we aimed to investigate the prognostic redox-associated genes and construct a redox-based prognostic signature for LUAD.

Materials and Methods

A discovery cohort containing 479 LUAD samples from The Cancer Genome Atlas (TCGA) was analyzed. We identified prognostic redox-associated genes by weighted correlation network analysis (WGCNA) and univariate Cox regression analysis to construct a prognostic model via least absolute shrinkage and selection operator (LASSO)-multivariate Cox regression analyses. The performance of the redox-based model was validated in the TCGA cohort and an independent cohort of 456 samples by Cox regression analyses, log-rank test, and receiver operating characteristic (ROC) curves. Correlations of the model with clinicopathological variables and lymphocyte infiltration were assessed. Gene set enrichment analysis (GSEA) was used to clarify the underlying mechanism of the prognostic model. We constructed a nomogram based on the model and created calibration curves to show the accordance between actual survival and predicted survival of the nomogram.

Results

Stepwise analyses identified 6 prognostic redox-associated genes of LUAD and constructed a prognostic model that performed well in both the discovery and validation cohorts. The model was found to be associated with tumor stage, mutation of TP53 and EGFR, and lymphocyte infiltration. The model was mainly involved in the regulation of the cell cycle, DNA replication and repair, NADH metabolism, and the p53 signaling pathway. Calibration curves showed the high predictive accuracy of the nomogram.

Conclusions

This study explored the role of redox-associated genes in LUAD and constructed a prognostic model of LUAD. The signature was also associated with tumor progression and therapeutic response to immunotherapy. These findings contributed to uncovering the underlying mechanism and discovering novel prognostic predictor of LUAD.