Fig 1.
The diagram depicts the process of research, analysis, and the information flow.
Sample information was extracted following the examination of publicly available datasets: GSE76925, GSE24206, and GSE18842. Both healthy and diseased cell populations were represented within the sample data. The samples obtained from 11 IPF-affected patients undergoing lung transplantation or diagnostic surgical biopsy are included in the GSE24206 dataset. There are 91 samples of NSCLC in the GSE18842 collection. In the GSE76925 cohort, including 40 as control subjects and 111 infected patients, 214 genes were found to be differentially expressed. From these datasets, the analysis conducted in R facilitated the identification of common DEGs. These common genes were then used to study KEGG pathways, pharmacological signatures, and Protein-Protein Interactions (PPI) networks.
Table 1.
Collection of differentially expressed genes (DEGs) associated with the targeted diseases obtained from the GEO datasets in the NCBI database. The DEGs are arranged in ascending order based on their assigned weights.
Fig 2.
(A) Gene expression profiles of IPF-affected tissues were analysed using 23 samples selected from the GSE24206 dataset, with a focus on the top 20 genes.
(B) 23 samples were chosen from the GSE76925 dataset, and the expression of the top 20 genes in tissues impacted by COPD was evaluated. (C) Using 26 samples selected from the GSE18842 dataset, the expression of the top 20 genes in lung cancer-affected tissues was examined. The distinct expression patterns of these important genes in each of the three diseases are graphically depicted in this heatmap. This kind of comparative research helps distinguish between distinct and overlapping genetic markers.
Fig 3.
An extensive summary of the alterations in gene expression in each disease state is given by these maps.
Potential research targets can be identified by highlighting the genes that have undergone the greatest alteration. (A) A volcano plot illustrates the up- and down-regulated gene regulation for the GSE24206 IPF dataset. (B) A volcano map illustrates the gene regulation (up and down) in COPD accession GSE76925. (C) The volcano graphic, which illustrates gene regulation, uses the context of the GSE18842 lung cancer dataset to display both upregulated and downregulated genes.
Fig 4.
(A) The diagram named venn depicts the overlap of DEGs identified across the three conditions.
The GSE24206, GSE76925, and GSE18842 datasets were discovered to share eight DEGs through the identification of shared genes. The possible use of similar genes as biomarkers or targets for treatment is emphasised by this analysis, which is further supported by their regular variation in expression through a variety of lung diseases. (B) A heat map visualisation of the genes common among these three conditions. The heatmap was produced by log-fold transforming the top four DEGs, which are frequently common among the datasets for lung cancer, IPF, and COPD.
Table 2.
Moreover, in addition to the list of P-values, an in-depth investigation was conducted in relation to the association between common proteins as well as proteins linked with Gene Ontology terms and Gene Ontology (GO) networks. The investigation carried out in the study also identified enrichment in terms of biological processes, cellular components, and molecular functions that are linked with the genes of importance that were identified in the process. The P-values shown are evidence in statistics that highlight the key roles of key GO keywords and pathways that signify the importance of the (molecular) processes that lie behind them.
Table 3.
The researchers then did a thorough analysis to find the significance of the association between the P-values and shared genes in the KEGG, WikiPathways, Reactome, and BioCarta databases. This current study found some significant biological processes that support disease mechanisms by emphasising pathways with high numbers of shared genes. Such pathways may help in the development of the disease itself, where the P-values can serve as significant supportive evidence for these pathways’ roles in the disease’s development. Further study on these pathways can help in finding genetic interactions for the pathogenesis of the disease itself.
Fig 5.
(A) Biological processes: The analysis illustrates that DEGs were categorised into relevant biological processes using Gene Ontology (GO) analysis.
The focus of this figure is on the functions that DEGs play in pathways such as immune response, metabolism, and regulation of genes. (B) Molecular processes: This analysis utilised Gene Ontology (GO) research and showed that DEGs have a key role in molecular processes such as protein binding, catalytic activity, and signal transduction. (C) Cellular component: This figure illustrates that DEGs were categorised by cellular components through Gene Ontology (GO) analysis, which highlighted their distribution in structures such as the plasma membrane, cytoplasm, and nucleus.
Fig 6.
(A) KEGG pathway analysis: Using P-values, the KEGG pathway analysis acknowledged crucial biological pathways that included DEGs, some of which are key signalling pathways and metabolic functions.
(B) WikiPathways analysis: This is a representation of the WikiPathways analysis that acknowledged the significant pathways associated with the DEGs using biological processes and disease pathways that are significantly represented. (C) Reactome pathway analysis: This analysis acknowledges that there are key pathways affected by the DEGs, with a focus on the immune system and signalling functions. (D) BioCarta pathway analysis: Using P-values, the pathway analysis acknowledged the increased signalling functions of the DEGs, which provides information on cell functions and disease mechanisms.
Fig 7.
This figure represents PPI network analysis among three diseases, which provides a total of eight genes mutual to all three diseases.
All of these were identified as differentially expressed genes. Among these genes are CFH, MSH2, SORD, NEDD9, CCNL1, RORA, ETS1, and PMAIP1. The above genes show prominent interaction as well as shared biological roles, which are also known to play an important role within the pathophysiology of the conditions listed above. This study brings the spotlight on the varied interaction patterns that these genes demonstrate with each other, thus allowing them to play a crucial part in the development of these conditions through these interaction pathways. In this study, understanding these interaction patterns will help scientists gain insights into these cellular events, thus allowing the identification of further targets for research-driven treatment options for these conditions. Using these networks, researchers can thus create better and more functional assessments of shared DEGs, thereby establishing a basis for developing more targeted treatment options.
Table 4.
Based on topological outcomes, the top four hub genes that are identified as ETS1, MSH2, RORA, and PMAIP1 have been given below. The results indicate that these genes play a significant role in the network and suggest that they have great potential for therapeutic applications. The topological outcomes have helped identify that these genes are significant components of MCODE networks due to their high interaction levels within protein-protein interaction networks, indicating their great potential for therapeutic applications. This data not only reveals the structural significance of these parts but also emphasises the great potential for utilising them as markers and therapeutic targets in the field of health.
Fig 8.
The figure illustrate how hub genes are identified from the common differentially expressed genes across the three given conditions.
(ETS1 and MSH2) and (RORA and PMAIP1) are significant nodes. This network highlights the importance these genes have in the whole network. Our significant degree of connection clearly indicates how important their roles are in basic pathways, and that is why they act as hubs. Identifying these genes at once shows their function and how they can be used as variables, as they are important in this network.
Fig 9.
A comprehensive assessment of the diagnostic capabilities of the candidate genes was performed using ROC curve analysis.
(a) ROC curves depicting the performance of the common hub genes in the GSE33532 dataset; (b) ROC curves for the same genes in the GSE40791 dataset. The findings highlight these genes’ strong capability to differentiate tumour samples from normal ones, reinforcing their potential as reliable diagnostic biomarkers.
Fig 10.
Survival analysis of key genes, including ETS1, MSH2, RORA, and PMAIP1, has been generated by the product limit (PL) estimator with the study of common DEGs in IPF, COPD, and LC.
The red colour denotes normal expression, blue underexpression, and green overexpression with these graphs. People with modified gene expression have a lower rate of survival compared to those with normal gene expression, as demonstrated vividly by the survival variances between the two groups. Concerning these results, ETS1, MSH2, RORA, and PMAIP1 might be significant biomarkers to evaluate patient outcomes in disease diagnosis. These particular genes ought to be the focus of further research to figure out the roles they play in diseases and to create targeted treatments, considering the reported survival imbalances indicate that their function is essential.
Fig 11.
This figure illustrates tf-gene interaction, which is based on common DEGs; nodes that are pink in colour denote common DEGs, whereas nodes that are light green represent tf-genes.
The aforementioned depiction additionally shows significant regulatory pathways related to disease generation, but it also shows the regulatory connections between transcription factors, as well as the particular genes that are addressed. By discovering these correlations, the network reveals mechanisms involved in transcriptional regulation and therapeutic strategies. By assigning each node a different colour for analysis, it becomes possible to distinguish which DEGs are relevant and which TFs are involved in them; this process reveals the correlation between these factors, as well as disease mechanisms.
Fig 12.
This figure shows the TF-miRNA regulation network, which has been coloured to identify the responsibilities of the nodes: the yellow nodes represent miRNAs, the red nodes denote common differentially expressed genes (DEGs), and the remaining nodes correspond to the other DEGs.
The dynamic regulatory networks composed of transcriptional factors (TFs), microRNAs (miRNAs), and DEGs are depicted by colours in this diagram. By demonstrating each of these relationships in turn, the network narrows its focus to significant miRNAs, which may serve as powerful regulators, controlling the gene expression of various DEGs concurrently. Understanding such regulatory interactions is very essential for identifying drug targets and developing means of modifying gene activity in disease contexts. The network sets the ground to uncover miRNAs that execute an extensive range of regulatory activities that may be a useful tool in searching for specific therapies directed toward the regeneration of appropriate gene expression profiles in disease.
Table 5.
By analysing the shared DEGs, molecules related to IPF, COPD, and lung cancer have been detected based on drug suggestions. By this method, new pharmacologic agents have been recognised for targeting shared DEGs as potential treatments. These new pharmacologic agents have provided promising results in curing different types of lung diseases. The verification stage and drug development process will be explored in identifying molecules based on the new pharmacologic agents. By identifying common biological features, this approach promises to develop more efficient, multi-conditional, wide-spectrum therapies.