Primary Sjogren's Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification
September 20th, 2024
This manuscript describes the operational procedures and precautions to probe the potential common pathogenic mechanisms linking primary Sjogren's syndrome and lung adenocarcinoma through bioinformatics analysis and experimental verification.
My research explores the potential common pathogenic mechanisms linking primary Sjogren's syndrome and adenocarcinoma through bioinformatics insights and experimental verification. I aim to understand how these conditions interject and the underlying factors contributing to the relationship. My research addresses the gap in understanding the link between primary Sjogren syndrome and adenocarcinoma, highlighting their shared pathogenic mechanisms.
This insight came from future diagnostic and therapeutic strategies for effected patients. In future research, we hope to collaborate with thoracic surgery to obtain pulmonary surgical samples from pSS patients to illustrate the progression from lymphocyte infiltration to neoplastic proximal sites To begin, open the Gene Expression Omnibus or GEO database and use primary Sjogren's syndrome and lung adenocarcinoma as keywords to search for gene expression profiles. Click on the results in the GEO DataSets database and select Homo sapiens in Top Organisms.
Select and download the dataset of interest, along with its corresponding platform information. Then open the GeneCard database and use primary Sjogren's syndrome and lung adenocarcinoma as keywords to obtain the genes related to pSS and LUAD. Download the spreadsheets of the disease genes.
Using R software, identify and visualize differentially expressed genes or DEGs with an adjusted P value of less than 0.05 and a fold change greater than 1.2 or less than 0.83. Compare and analyze gene expression among these datasets. Select genes with an expression level greater than or equal to 20 related to pSS and LUAD from the GeneCard database.
Merge the DEGs associated with pSS and LUAD from both the GEO database and the GeneCard database. After installing Venn Diagram package in R software, obtain and visualize the DEGs associated with pSS and LUAD. In the Metascape website, click Select File and upload the xlsx format file of pSS-LUAD-DEGs.
Choose Homo sapiens for both Input as species and Analysis as species. Click Custom Analysis, then click Enrichment and select KEGG pathway. Uncheck the other options.
Click Enrichment Analysis, and once complete, click Analysis Report Page. Click All in One Zip File to download the results. Access the _FINAL_GO.
csv file in the Enrichment_GO folder to view the results. Using the ggplot2 package in R software, perform the KEGG visualization program. For gene ontology or GO enrichment analysis, import the text format list of pSS-LUAD-DEGs into R.Run the Cluster Profiler and Enrich Plot packages for GO enrichment analysis and result visualization.
Set statistical significance in the analysis at an adjusted P value of less than 0.05. Access the STRING database and click Browse, and upload the file of pSS-LUAD-DEGs. Select Homo sapiens in Organisms, then click Search.
After clicking Continue, when the results are available, click on Settings. Under Basic Settings and minimum required interaction score, select high confidence 0.7. Tick hide disconnected nodes in the network in Advanced Settings, then click Update.
Click on Exports in the title bar to download the protein-protein interaction or PPI relationship text in TSV format. In the Cytoscape 3.7.1 software, click File, followed by Import and Network from File to import the TSV format file for constructing the PPI network. Use the NetworkAnalyzer tool to analyze the topological parameters in the network and optimize node size and color via the Style bar in the control panel.
On the menu bar, select Tools and Analyze Network. In the table panel, click on Degree to sort components by degree in descending order. For the identification and validation of hub genes, import the text format list of hub genes into R loaded with PROC package.
Plot receiver operating characteristic or ROC curves of the hub genes, and calculate the area under the ROC curve values. A total of 233 shared DEGs between pSS DEGs and LUAD DEGs were visualized using a Venn diagram after the analysis. The top 10 significant KEGG pathways for pSS LUAD DEGs were identified and primarily associated with metabolic pathways, including PI3K-Akt, MAPK, and cytokine-cytokine receptor interactions.
GO enrichment analysis of pSS LUAD DEGs revealed significant biological processes including response to virus and innate immune response, cell component categories, and molecular functions like cytokine receptor binding and activity. The PPI network comprised 99 nodes and 466 edges, identifying STAT3, STAT1, and TP53 as the top hub genes. The top 20 genes with higher degrees in the PPI network were visualized, including TNF, IL6, and EGFR.
To begin, perform bioinformatics analysis to determine the inflammatory pathways in the co-occurrence of primary Sjogren's syndrome and lung adenocarcinoma. For developing the animal model, position an anesthetized mouse on a small animal restrainer with its abdomen facing upwards, head elevated, and tail lowered at a 45-degree angle. Use fine thread to loop around the mouse's upper incisors, pulling them upwards, and secure the thread onto a screw on the animal holder to fully expose the mouse's oral cavity.
After turning on the cold light lamp, use forceps to gently pull out the mouse's tongue and fully expose the glottis. Insert an 18-gauge venous-indwelling needle into the mouse trachea and pull out the needle core. Place a cotton thread at the outer end of the needle, and confirm successful insertion when the thread moves with the mouse's chest movements.
Using a one-milliliter syringe, aspirate 0.2 milliliters of air, then 0.1 milliliters of PM 2.5 suspension, and finally, another 0.2 milliliters of air. Inject the mixture into the trachea through the 18-gauge venous-indwelling needle. After pulling out the indwelling needle, secure the animal holder upright and rotate it clockwise and counterclockwise 30 times to evenly distribute the PM 2.5 suspension in the lungs.
Extend the mouse's neck straight and lay it on its side to prevent suffocation, and let it recover. After 29 days, place the euthanized mouse in a supine position on a clean dissecting board. Using scissors and forceps, remove the skin and muscles covering the ventral, thoracic, and neck regions.
Make incisions along the edges of the ribs on both sides of the chest cavity to expose the thoracic cavity. Cut the clavicle to create a wide enough opening for examination of the lung lobes. Excise the neck muscles extending from the sternum and ribs to the jaw.
Insert scissors below the anterior edge of the ribs, and make incisions on both sides to remove the bony portion covering the trachea. Grasp the trachea near the jaw with forceps and make a complete transverse incision using scissors placed above the forceps. Then gently pull up the trachea with forceps, cutting ventral tissue connections with scissors until the entire thoracic tissue is removed from the body.
Lay the lungs flat and rinse the surface with saline. After blotting it dry, aliquot the tissue into cryotubes to store at minus 80 degrees Celsius for future biochemical use.
Overview
This manuscript explores the common pathogenic mechanisms linking primary Sjogren's syndrome and lung adenocarcinoma through bioinformatics analysis and experimental verification. The research aims to bridge the gap in understanding the relationship between these conditions.
Key Study Components
Area of Science
- Neuroscience
- Oncology
- Bioinformatics
Background
- Primary Sjogren's syndrome is an autoimmune condition.
- Lung adenocarcinoma is a type of lung cancer.
- Understanding their link can improve patient outcomes.
- Research highlights shared pathogenic mechanisms.
Purpose of Study
- To investigate the relationship between primary Sjogren's syndrome and lung adenocarcinoma.
- To identify common pathogenic mechanisms.
- To inform future diagnostic and therapeutic strategies.
Methods Used
- Bioinformatics analysis of gene expression profiles.
- Experimental verification of findings.
- Collaboration with thoracic surgery for sample collection.
- Utilization of the Gene Expression Omnibus (GEO) database.
Main Results
- Identification of gene expression profiles linking both conditions.
- Insights into the progression from lymphocyte infiltration to neoplasia.
- Potential for future research collaborations.
- Implications for patient diagnosis and treatment.
Conclusions
- Shared pathogenic mechanisms exist between primary Sjogren's syndrome and lung adenocarcinoma.
- Research findings could lead to improved patient management.
- Future studies are necessary to further explore these links.
Frequently Asked Questions
Executive Industry Relevance
Integrating bioinformatics with experimental validation, this study addresses the mechanistic overlap between primary Sjogren's syndrome (pSS) and lung adenocarcinoma (LUAD), focusing on inflammation-driven carcinogenesis. The identification of shared molecular pathways and hub genes informs early discovery and target validation, supporting risk-adjusted portfolio decisions in immuno-oncology and autoimmune disease pipelines. These insights enable translational continuity from hypothesis generation to preclinical model development for dual-disease risk mitigation.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Bioinformatics-driven identification of shared DEGs enables mechanistic de-risking for both autoimmune and oncology targets.
- PPI network analysis clarifies pathway convergence, supporting functional target validation.
- Hub gene ROC analysis provides predictive confidence for prioritizing candidate targets.
Screening & Assay Development
- Validated animal models and molecular assays (qPCR, ELISA, western blot) establish reproducible systems for downstream screening.
- Quantitative cytokine and gene expression outputs enable assay standardization and cross-study comparability.
- Pathway activation markers (e.g., JAK2/STAT3) support screening readiness for pathway-modulating compounds.
Translational & Preclinical Research
- Inflammatory microenvironment modeling in vivo aligns with disease-relevant systems for translational biomarker exploration.
- Gene and protein expression data bridge discovery findings to preclinical validation in autoimmune-oncology overlap.
- Risk-adjusted advancement decisions are informed by mechanistic links between chronic inflammation and tumorigenesis.
Pipeline & Workflow Integration
This workflow spans early discovery through preclinical model validation, integrating bioinformatics, molecular profiling, and in vivo experimentation for dual-disease risk assessment.
- Discovery Biology: Hypothesis testing of shared inflammatory and oncogenic pathways supports biological de-risking.
- Screening: Quantitative molecular outputs enable reproducible assay development for pathway-targeted interventions.
- Analytics: Statistical enrichment, PPI network metrics, and ROC analyses provide robust comparative readouts.
- Translational Research: In vivo inflammatory models facilitate biomarker alignment and translational continuity.
- Enterprise Reuse: The integrated workflow is adaptable for other autoimmune-cancer comorbidity investigations.
Operational & Enterprise Impact
- Scientific Value: Enhanced predictive confidence and mechanistic clarity for target validation in complex disease intersections.
- Operational Value: Standardized, scalable bioinformatics and experimental protocols improve reproducibility and cross-team integration.
- Strategic Value: Informed go/no-go decisions and capital allocation for dual-indication programs.
- Portfolio Impact: Supports risk-adjusted prioritization of assets targeting inflammation-driven oncogenesis.
Implementation Considerations
- Requires expertise in bioinformatics, molecular biology, and in vivo model development.
- Demands access to high-throughput gene expression platforms and analytical software (R, Cytoscape, Metascape).
- Cross-functional standardization is essential for data integration and reproducibility.
- Model adaptation may be needed for other autoimmune or cancer contexts.
- Interpretation of pathway activation and biomarker data must consider disease heterogeneity.
Biopharma FAQs
Chapters in this video
0:00
Introduction
1:09
Bioinformatics Analysis of Shared Inflammatory Mechanisms in Primary Sjogren's Syndrome and Lung Adenocarcinoma
7:07
Developing an Animal Model for Verifying Common Pathogenic Mechanisms Linking Primary Sjogren's Syndrome and Lung Adenocarcinoma Identified Through Bioinformatics
