Integrative analyses of genes associated with idiopathic pulmonary fibrosis

Prediction model of mitochondrial energy metabolism related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease. Recent evidence suggests that the pathogenesis of IPF may involve abnormalities in mitochondrial energy metabolism. This study aimed to identify mitochondrial energy metabolism related differentially expressed genes (MEMRDEGs) and to elucidate their potential mechanistic involvement in IPF. We employed a multistep bioinformatics approach, including data extraction from the Gene Expression Omnibus database, removal of batch effects, and normalization and differential gene expression analyses. We then conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses. A protein-protein interaction network was constructed from the STRING database, and hub genes were identified. Receiver operating characteristic curve analysis was performed to evaluate immune infiltration. Our integrated analysis of IPF datasets identified 25 MEMRDEGs. Nine hub genes emerged as central to mitochondrial energy metabolism in IPF. COX5A, EHHADH, and SDHB are potential biomarkers for diagnosing IPF with high accuracy. Single-sample gene set enrichment analysis revealed significant differences in the abundances of specertainfic immune cell types between IPF samples and controls. In conclusion, COX5A, EHHADH, and SDHB are potential biomarkers for the high-accuracy diagnosis of IPF. These findings pave the way for further investigations into the molecular mechanisms underlying IPF.

Identification of Crohn's Disease-Related Biomarkers and Pan-Cancer Analysis Based on Machine Learning

Background: In recent years, the incidence of Crohn's disease (CD) has shown a significant global increase, with numerous studies demonstrating its correlation with various cancers. This study aims to identify novel biomarkers for diagnosing CD and explore their potential applications in pan-cancer analysis. Methods: Gene expression profiles were retrieved from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified using the "limma" R package. Key biomarkers were selected through an integrative machine learning pipeline combining LASSO regression, neural network modeling, and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Six hub genes were identified and further validated using the independent dataset GSE169568. To assess the broader relevance of these biomarkers, a standardized pan-cancer dataset from the UCSC database was analyzed to evaluate their associations with 33 cancer types. Results: Among the identified biomarkers, S100 calcium binding protein P (S100P) and S100 calcium binding protein A8 (S100A8) emerged as key candidates for CD diagnosis, with strong validation in the independent dataset. Notably, S100P displayed significant associations with immune cell infiltration and patient survival outcomes in both liver and lung cancers. These findings suggest that chronic inflammation and immune imbalances in CD may not only contribute to disease progression but also elevate cancer risk. As an inflammation-associated biomarker, S100P holds particular promise for both CD diagnosis and potential cancer risk stratification, especially in liver and lung cancers. Conclusion: Our study highlights S100P and S100A8 as potential diagnostic biomarkers for CD. Moreover, the pan-cancer analysis underscores the broader clinical relevance of S100P, offering new insights into its role in immune modulation and cancer prognosis. These findings provide a valuable foundation for future research into the shared molecular pathways linking chronic inflammatory diseases and cancer development.

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible disease with a high mortality rate worldwide. However, the etiology and pathogenesis of IPF have not yet been fully described. Moreover, lung cancer is a significant complication of IPF and is associated with increased mortality. Nevertheless, identifying common genes involved in developing IPF and its progression to lung cancer remains an unmet need. The present study aimed to identify hub genes related to the development of IPF by meta-analysis. In addition, we analyzed their expression and their relationship with patients' progression in lung cancer.

METHOD

Microarray datasets GSE24206, GSE21369, GSE110147, GSE72073, and GSE32539 were downloaded from Gene Expression Omnibus (GEO). Next, we conducted a series of bioinformatics analysis to explore possible hub genes in IPF and evaluated the expression of hub genes in lung cancer and their relationship with the progression of different stages of cancer.

RESULTS

A total of 1888 differentially expressed genes (DEGs) were identified, including 1105 upregulated and 783 downregulated genes. The 10 hub genes that exhibited a high degree of connectivity from the PPI network were identified. Analysis of the KEGG pathways showed that hub genes correlate with pathways such as the ECM-receptor interaction. Finally, we found that these hub genes are expressed in lung cancer and are associated with the progression of different stages of lung cancer.

CONCLUSIONS

Based on the integration of GEO microarray datasets, the present study identified DEGs and hub genes that could play an essential role in the pathogenesis of IPF and its association with the development of lung cancer in these patients, which could be considered potential diagnostic biomarkers or therapeutic targets for the disease.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

RTP4, a Biomarker Associated with Diagnosing Pulmonary Tuberculosis and Pan-Cancer Analysis

Background

Pulmonary tuberculosis (PTB) is a global epidemic of infectious disease; the purpose of our study was to explore new potential biomarkers for the diagnosis of pulmonary tuberculosis and to use the biomarkers for further pan-cancer analysis.

Methods

Four microarray gene expression sets were downloaded from the GEO public databases and conducted for further analysis. Healthy control (HC) samples and samples of pulmonary tuberculosis (PTB) were calculated with enrichment scores in folate biosynthesis pathways. The scores acted as a new phenotype combined with clinical information (control or PTB) for subsequent analysis. Weight gene coexpression network analysis (WGCNA) was used to seek the modules mostly related to PTB and folate biosynthesis in training sets. Twenty-nine coexistence genes were screened by intersecting the genes in the green-yellow module of GSE28623 and the brown module of GSE83456. We used the protein-protein interaction network analysis to narrow the gene range to search for hub genes. Then, we downloaded the unified and standardized pan-cancer data set from the UCSC database for correlations between biomarkers and prognosis and tumor stage differences.

Results

Eventually, RTP4 was selected as a biomarker. To verify the reliability of this biomarker, an area under the ROC (AUC) was calculated in gene sets (GSE28623, GSE83456, and GSE34608). Lastly, to explore the difference in RTP4 expression before and after antituberculosis treatment, the GSE31348 gene set was enrolled to compare the expressions in weeks 0 and 26. The results showed significant differences between these two time points (p < 0.001). RTP4 was significantly upregulated in the pulmonary tuberculosis group compared to the healthy control group in three gene sets and downregulated after antituberculosis therapy in one gene set. These results suggest that RTP4 can be used as a potential biomarker in diagnosing tuberculosis. The results of pan-cancer analysis showed that high expression of RTP4 in 4 tumor types was positively correlated with poor prognosis and high expression of RTP4 in 6 tumor types was negatively correlated with poor prognosis. We found significant differences in the expression of the RTP4 gene at different stages in 5 types of tumors.

Conclusion

RTP4 might be a new potential biomarker for diagnosing pulmonary tuberculosis.

GBP2 acts as a member of the interferon signalling pathway in lupus nephritis

Lupus nephritis (LN) is a common and serious clinical manifestation of systemic lupus erythematosus. However, the pathogenesis of LN is not fully understood. The currently available treatments do not cure the disease and appear to have a variety of side effects in the long term. The purpose of this study was to search for key molecules involved in the LN immune response through bioinformatics techniques to provide a reference for LN-specific targeted therapy. The GSE112943 dataset was downloaded from the Gene Expression Omnibus database, and 20 of the samples were selected for analysis. In total, 2330 differentially expressed genes were screened. These genes were intersected with a list of immune genes obtained from the IMMPORT immune database to obtain 128 differentially expressed immune-related genes. Enrichment analysis showed that most of these genes were enriched in the interferon signalling pathway. Gene set enrichment analysis revealed that the sample was significantly enriched for expression of the interferon signalling pathway. Further analysis of the core gene cluster showed that nine genes, GBP2, VCAM1, ADAR, IFITM1, BST2, MX2, IRF5, OAS1 and TRIM22, were involved in the interferon signalling pathway. According to our analysis, the guanylate binding protein 2 (GBP2), interferon regulatory factor 5 and 2′-5′-oligoadenylate synthetase 1 (OAS1) genes are involved in three interferon signalling pathways. At present, we do not know whether GBP2 is associated with LN. Therefore, this study focused on the relationship between GBP2 and LN pathogenesis. We speculate that GBP2 may play a role in the pathogenesis of LN as a member of the interferon signalling pathway. Further immunohistochemical results showed that the expression of GBP2 was increased in the renal tissues of LN patients compared with the control group, confirming this conjecture. In conclusion, GBP2 is a member of the interferon signalling pathway that may have implications for the pathogenesis of LN and serves as a potential biomarker for LN.

Niclosamide Ethanolamine Salt Alleviates Idiopathic Pulmonary Fibrosis by Modulating the PI3K-mTORC1 Pathway

Idiopathic pulmonary fibrosis (IPF) is an interstitial pneumonia characterized by chronic progressive fibrosis, ultimately leading to respiratory failure and early mortality. Although not fully explored, the major causative factors in IPF pathogenesis are dysregulated fibroblast proliferation and excessive accumulation of extracellular matrix (ECM) deposited by myofibroblasts differentiated from pulmonary fibroblasts. More signalling pathways, including the PI3K-Akt-mTOR and autophagy pathways, are involved in IPF pathogenesis. Niclosamide ethanolamine salt (NEN) is a highly effective multitarget small-molecule inhibitor reported in antitumor studies. Here, we reported that in an IPF animal model treated with NEN for 14 days, attractive relief of pulmonary function and hydroxyproline content were observed. To further explore, the therapeutic effect of NEN in IPF and pathological changes in bleomycin-challenged mouse lung sections were assessed. Additionally, the effects of NEN on abnormal proliferation and ECM production in IPF cell models established with TGF-β1-stimulated A549 cells or DHLF-IPF cells were studied. In nonclinical studies, NEN ameliorated lung function and histopathological changes in bleomycin-challenged mice, and the lung hydroxyproline content was significantly diminished with NEN treatment. In vitro, NEN inhibited PI3K-mTORC1 signalling and arrested the cell cycle to prevent uncontrolled fibroblast proliferation. Additionally, NEN inhibited TGF-β1-induced epithelial–mesenchymal transition (EMT) and ECM accumulation via the mTORC1-4EBP1 axis. Furthermore, NEN-activated noncanonical autophagy resensitized fibroblasts to apoptosis. The above findings demonstrated the potential antifibrotic effect of NEN mediated via modulation of the PI3K-mTORC1 and autophagy pathways. These data provide strong evidence for a therapeutic role for NEN in IPF.

Identification of the Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Gene Expression Profiles

Background

Idiopathic Pulmonary Fibrosis (IPF) is one of the most common idiopathic interstitial pneumonia, which can occur all over the world. The median survival time of patients is about 3-5 years, and the mortality is relatively high.

Objective

To reveal the potential molecular characteristics of IPF and deepen the understanding of the molecular mechanism of IPF. In order to provide some guidance for the clinical treatment, new drug development, and prognosis judgment of IPF. Although the preliminary conclusion of this study has certain guiding significance for the treatment of IPF and so on, it needs more accurate analytical approaches and large sample clinical trials to verify.

Methods

220 patients with IPF were divided into different subgroups according to the gene expression profiles, which were obtained from the Gene Expression Omnibus (GEO) database. In addition, these subgroups present different expression forms and clinical features. Therefore, weighted gene coexpression analysis (WGCNA) was used to seek the differences between subtypes. And six subgroup-specific WGCNA modules were identified.

Results

Combined with the characteristics of WGCNA and KEGG enrichment modules, the autophagic pathway was only upregulated in subgroup I and enriched significantly. The differentiation pathways of Th1 and Th2 cells were only upregulated and enriched in subgroup II. At the same time, combined with clinical information, IPF patients in subgroup II were older and more serious, which may be closely related to the differentiation of Th1 and Th2 cells. In contrast, the neuroactive ligand-receptor interaction pathway and Ca⁺ signaling pathway were significantly upregulated and enriched in subgroup III. Although there was no significant difference in prognosis between subgroup I and subgroup III, their intrinsic biological characteristics were very different. These results suggest that the subtypes may represent risk factors of age and intrinsic biological characteristics and may also partly reflect the severity of the disease.

Conclusion

In conclusion, current studies have improved our understanding of IPF-related molecular mechanisms. At the same time, because the results show that patients from different subgroups may have their own unique gene expression patterns, it reminds us that patients in each subgroup should receive more personalized treatment.

IRF1 as a potential biomarker in Mycobacterium tuberculosis infection

Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491 and GSE50834 are blood microarray. The differential genes of GSE139825 and GSE83456 were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell-cell adhesion and regulation of T-cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine-cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein-protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up-regulated in patients with TB relative to healthy controls in data sets GSE19491 and GSE50834. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up-regulated in patients with TB compared with healthy controls with an AUC of 0.801. These results collectively indicate that IRF1 could serve as a new biomarker for the diagnosis of pulmonary tuberculosis.

Spatially Resolved Identification of Transglutaminase Substrates by Proteomics in Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is characterized by the invariably progressive deposition of fibrotic tissue in the lungs and overall poor prognosis. Transglutaminase 2 (TG2) is an enzyme that crosslinks glutamine and lysine residues and is involved in IPF pathogenesis. Despite the accumulating evidence implicating TG2 as a critical enzyme, the causative function and direct target of TG2 relating to this pathogenesis remain unelucidated. Here, we clarified the distributions of TG2 protein/activity and conducted quantitative proteomics analyses of possible substrates crosslinked by TG2 on unfixed lung sections in a mouse pulmonary fibrosis model. We identified 126 possible substrates as markedly increased TG2-dependently in fibrotic lung. Gene ontology analysis revealed that these identified proteins were mostly enriched in the lipid metabolic process, immune system process, and protein transport. In addition, these proteins enriched in the 21 pathways including phagosome, lipid metabolism, several immune responses, and protein processing in endoplasmic reticulum. Furthermore, the network analyses screened out the 6 clusters and top 20 hub proteins with higher scores, which are related to ER stress and peroxisome proliferator-activated receptor signals. Several enriched pathways and categories were identified, and some of which were the same terms based on transcription analysis in IPF. Our results provide novel pathological molecular networks driven by protein crosslinking via TG2, which can lead to the development of new therapeutic targets for IPF.

Identification and Validation of Potential Biomarkers and Pathways for Idiopathic Pulmonary Fibrosis by Comprehensive Bioinformatics Analysis

Objective

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, irreversible, high-mortality lung disease, but its pathogenesis is still unclear. Our purpose was to explore potential genes and molecular mechanisms underlying IPF.

Methods

IPF-related data were obtained from the GSE99621 dataset. Differentially expressed genes (DEGs) were identified between IPF and controls. Their biological functions were analyzed. The relationships between DEGs and microRNAs (miRNAs) were predicted. DEGs and pathways were validated in a microarray dataset. A protein-protein interaction (PPI) network was constructed based on these common DEGs. Western blot was used to validate hub genes in IPF cell models by western blot.

Results

DEGs were identified for IPF than controls in the RNA-seq dataset. Functional enrichment analysis showed that these DEGs were mainly enriched in immune and inflammatory response, chemokine-mediated signaling pathway, cell adhesion, and other biological processes. In the miRNA-target network based on RNA-seq dataset, we found several miRNA targets among all DEGs, like RAB11FIP1, TGFBR3, and SPP1. We identified 304 upregulated genes and 282 downregulated genes in IPF compared to controls both in the microarray and RNA-seq datasets. These common DEGs were mainly involved in cell adhesion, extracellular matrix organization, oxidation-reduction process, and lung vasculature development. In the PPI network, 3 upregulated and 4 downregulated genes could be considered hub genes, which were confirmed in the IPF cell models.

Conclusion

Our study identified several IPF-related DEGs that could become potential biomarkers for IPF. Large-scale multicentric studies are eagerly needed to confirm the utility of these biomarkers.

Gene signatures with predictive and prognostic survival values in human osteosarcoma

Osteosarcoma is a common malignancy seen mainly in children and adolescents. The disease is characterized by poor overall prognosis and lower survival due to a lack of predictive markers. Many gene signatures with diagnostic, prognostic, and predictive values were evaluated to achieve better clinical outcomes. Two public data series, GSE21257 and UCSC Xena, were used to identify the minimum number of robust genes needed for a predictive signature to guide prognosis of patients with osteosarcoma. The lasso regression algorithm was used to analyze sequencing data from TCGA-TARGET, and methods such as Cox regression analysis, risk factor scoring, receiving operating curve, KMplot prognosis analysis, and nomogram were used to characterize the prognostic predictive power of the identified genes. Their utility was assessed using the GEO osteosarcoma dataset. Finally, the functional enrichment analysis of the identified genes was performed. A total of twenty-gene signatures were found to have a good prognostic value for predicting patient survival. Gene ontology analysis showed that the key genes related to osteosarcoma were categorized as peptide–antigen binding, clathrin-coated endocytic vesicle membrane, peptide binding, and MHC class II protein complex. The osteosarcoma related genes in these modules were significantly enriched in the processes of antigen processing and presentation, phagocytosis, cell adhesion molecules, Staphylococcus aureus infection. Twenty gene signatures were identified related to osteosarcoma, which would be helpful for predicting prognosis of patients with OS. Further, these signatures can be used to determine the subtypes of osteosarcoma.

Small GTPase RAB6 deficiency promotes alveolar progenitor cell renewal and attenuates PM2.5-induced lung injury and fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by chronic non-specific inflammation of the interstitial lung and extensive deposition of collagen fibers leading to destruction of lung function. Studies have demonstrated that exposure to fine particulate matter (PM2.5) increases the risk of IPF. In order to recover from PM2.5-induced lung injury, alveolar epithelial cells need to be repaired and regenerated to maintain lung function. Type 2 alveolar epithelial cells (AEC2) are stem cells in the adult lung that contribute to the lung repair process through complex signaling. Our previous studies demonstrated that RAB6, a RAS family member lowly expressed in lung cancer, inhibited lung cancer stem cell self-renewal, but it is unclear whether or not and how RAB6 may regulate AEC2 cell proliferation and self-renewal in PM2.5-induced pulmonary fibrosis. Here, we demonstrated that knockout of RAB6 inhibited pulmonary fibrosis, oxidative stress, and AEC2 cell death in PM2.5-injured mice. In addition, knockout of RAB6 decreased Dickkopf 1(DKK1) autocrine and activated proliferation, self-renewal, and wnt/β-catenin signaling of PM2.5-injured AEC2 cells. RAB6 overexpression increased DKK1 autocrine and inhibited proliferation, self-renewal and wnt/β-catenin signaling in AEC2 cells in vitro. Furthermore, DKK1 inhibitors promoted proliferation, self-renewal and wnt/β-catenin signaling of RAB6 overexpressing AEC2 cells, and attenuated PM2.5-induced pulmonary fibrosis in mice. These data establish RAB6 as a regulator of DKK1 autocrine and wnt/β-catenin signal that serves to regulate AEC2 cell proliferation and self-renewal, and suggest a mechanism that RAB6 disruption may promote AEC2 cell proliferation and self-renewal to enhance lung repair following PM2.5 injury.

Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis

BACKGROUND

Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC.

METHODS

We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba.

RESULTS

We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network.

CONCLUSIONS

Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets for NPC.