Name: Primary Sjogrens Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification (Video) | JoVE Journal
Uploaded: 2024-09-20T15:00:00.000+00:00
Duration: 10 min 21 s
Description: Explore bioinformatics analysis linking primary Sjogren's syndrome and lung adenocarcinoma through gene expression profiling and experimental verification. This computational approach reveals shared pathogenic mechanisms.

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5 minutes

This study was supported by National High Level Hospital Clinical Research Funding (2023-NHLHCRF-BQ-01) and the Youth Project of China-Japan Friendship Hospital (No.2020-1-QN-8).

Key Molecular Targets Linking Primary Sjogren's Syndrome and Lung Adenocarcinoma

The authors have no conflicts of interest to disclose.

Although pSS is considered a disease primarily characterized by the invasion of exocrine glands, the damage of extra-glands cannot be ignored24. The lungs represent a target organ for pSS, and lung involvement is a common extra-glandular manifestation of pSS, typically involving lymphocytic infiltration of the bronchial mucosa and pulmonary interstitium25. Research indicates that at least 20% of pSS patients experience interstitial lung disease (ILD)26,27. ILD is a significant risk factor for lung cancer, which, to some extent, explains the increased incidence of lung cancer in pSS28. At the same time, pSS-ILD patients have a significant increase in tumor markers, further underscoring the association between pSS and pulmonary malignancies29,30. To explore the mechanisms of the association between pSS and LUAD, this study first conducted a bioinformatics analysis based on the DEGs associated with both pSS and LUAD and subsequently established animal models that are relevant to both diseases for experimental verification.
Following the identification of pSS-LUAD-DEGs, KEGG pathway enrichment analysis was conducted, revealing the critical role played by the PI3K/Akt signaling pathway in this association. The PI3K/Akt signaling pathway, upon activation by PI3K, catalyzes the generation of PIP3, recruiting PDK1 and AKT proteins to the plasma membrane. PDK1 phosphorylates AKT for activation, initiating a cascade of downstream effects. Related studies suggest that the involvement of the PI3K/Akt signaling pathway in the occurrence, proliferation, migration, apoptosis, and angiogenesis of tumors31,32,33,34,35,36. A series of clinical studies have demonstrated that the PI3K/Akt signaling pathway is widely activated in tumorigenesis36. Meanwhile, AKT can regulate the NF&#954;B signaling pathway by phosphorylating I&#954;B kinase, controlling the transcription of related inflammatory factors37,38. The MAPK signaling pathway39, cytokine-cytokine receptor interaction40, NOD-like receptor signaling pathway41, Th17 cell differentiation42, and JAK-STAT signaling pathway43 play crucial regulatory roles in inflammation-related processes. However, the FoxO signaling pathway44, necroptosis45, and cellular senescence46 impact tumorigenesis by regulating the apoptosis process in cells. Meanwhile, GO enrichment analysis has also revealed that biological processes closely related to inflammation, such as cytokine receptor binding, cytokine activity, tumor necrosis factor receptor superfamily binding, and tumor necrosis factor receptor binding, are involved in this process.
To further identify key targets between pSS and LUAD, core genes were obtained from pSS-LUAD-DEGs through PPI network analysis, followed by validation using the GSE31210 dataset. Ultimately, IL6, CCNA2, JAK2, IL1B, ASPM, CCNB2, NUSAP1, and CEP55 were determined as key targets of pSS-LUAD. IL6 and IL1B are key inflammatory factors that play critical roles in the inflammatory process of pSS47,48. Meanwhile, JAK2 serves as a key target in regulating the JAK-STAT signaling pathway, and in recent years, a series of JAK enzyme inhibitors have been attempted for the clinical treatment of pSS49. ASPM, CCNB2, NUSAP1, and CEP55 are involved in regulating DNA replication, thus playing crucial roles in the initiation and progression of tumors50,51,52,53,54,55. Studies have demonstrated that ASPM, CCNB2, NUSAP1, and CEP55 all promote the invasion and progression of LUAD51,52,55,56,57. The above study found that inflammation plays a crucial role in both pSS and LUAD. pSS characterized by lymphocytic infiltration, induces chronic and persistent inflammatory reactions in local tissues58. The occurrence and metastasis of lung cancer are closely associated with the inflammatory microenvironment of the lungs59. In summary, we hypothesized that inflammatory reaction may be a key to this association between pSS and LUAD. Using this as a reference point, further experimental verification was performed.
As no common model for pSS and LUAD currently exists, we decided to use PM2.5-stimulated pSS model mice to simulate a model relevant to both diseases after reviewing the relevant literature. PM2.5 refers to fine solid particles with a diameter of less than or equal to 2.5 &#181;m. It can float in the air and penetrate deep into the lungs, causing significant health damage60. Research indicates that long-term exposure to PM2.5 can lead to a chronic inflammatory microenvironment in the lungs, ultimately leading to the occurrence of LUAD21,61. NOD/Ltj mice, which exhibit lymphocytic infiltration into exocrine glands, are an ideal model for studying pSS62. Therefore, in further experimental verification, we stimulated NOD/Ltj mice with PM2.5 to establish a composite model of pSS and pulmonary inflammatory reactions. The experimental findings indicate that pro-inflammatory factors IL-6 and IL-1&#946; were barely expressed in both NOD/Ltj mice and BALB/c mice without PM2.5 stimulation. However, following PM2.5 stimulation, the expression of IL-6 and IL-1&#946; was significantly increased, indicating the successful establishment of the experimental model of pulmonary inflammatory microenvironment. The expression levels of IL-6 and IL-1&#946; were higher in NOD/Ltj mice compared to BALB/c mice following PM2.5 stimulation, indicating that pSS is more sensitive to the inflammatory reactions induced by PM2.5.
The JAK/STAT pathway is a signal transduction pathway stimulated by cytokines, participating in biological processes including cell growth, differentiation, apoptosis, and immune regulation63. Inflammatory cytokines, including IL-6 and IL-1&#946;, bind to receptors in the cell membrane, leading to the phosphorylation activation of Janus kinases (JAKs), and JAKs mediate STAT phosphorylation64. Phosphorylated STAT proteins transfer to the nucleus and regulate the expression of STAT-responsive genes. The JAK2/STAT3 signaling pathway is closely related to the regulation of tumor-related genes, which is activated mainly by IL-6 and modulates the expression of a series of proteins that promote cell growth, mobility, and tumor formation. This study revealed that following stimulation with PM2.5, the protein levels of JAK2 and STAT3 in the lungs of NOD/Ltj mice and BALB/c mice did not show significant changes, but the phosphorylation levels of JAK2 and STAT3 significantly increased, indicating JAK2/STAT3 pathway was abnormally activated. Further comparison revealed that the phosphorylation levels of JAK2 and STAT3 significantly increased in the lungs of NOD/Ltj mice compared to BALB/c mice, suggesting a more pronounced activation of the JAK2/STAT3 signaling pathway in the lungs of pSS mice under equivalent PM2.5 stimulation conditions.
Further exploration of tumor-related gene expression levels of CCNA2, ASPM, CCNB2, NUSAP1, and CEP55 as predicted by bioinformatics analysis. The results revealed that the expression levels of CCNA2, CCNB2, and CEP55 were elevated in the lungs following PM2.5 stimulation, with higher levels detected in NOD/Ltj mice compared to BALB/c mice. It suggested that CCNA2, CCNB2, and CEP55 might be closely related to inflammatory reactions following PM2.5 stimulation and involved in the potential occurrence and development of LUAD.
In future research, we hope to collaborate with thoracic surgery to obtain pulmonary surgical samples from pSS patients, including those with LUAD and non-cancerous lung lesions, to elucidate the progression from lymphocyte infiltration to neoplastic processes through single-cell sequencing. Meanwhile, inflammatory factors IL-6 and IL-1&#946;, along with the JAK/STAT signaling pathway crucial for regulating inflammation, have been associated with potential pathogenic mechanisms in the progression from pSS to LUAD. This indicates that drugs such as tocilizumab (IL-6 monoclonal antibodies), canakinumab (IL-1&#946; monoclonal antibodies), and tofacitinib (JAK inhibitors), which are related to inflammation, may serve as potential candidate drugs for preventing LUAD in pSS patients.

Primary Sj&#246;gren&#39;s syndrome (pSS) is an autoimmune disease characterized by lymphocytic infiltration of the exocrine glands and leads to the clinical symptoms of dry eyes (xerophthalmia) and dry mouth (xerostomia)1,2. pSS is also usually accompanied by extra-glandular manifestations of involvement, including hyperglobulinemia3, interstitial lung disease4, renal tubular acidosis5, neurological damage6, and thrombocytopenia7, which constitute the main adverse prognostic factors. In recent years, a line of studies has demonstrated that pSS is generally accompanied by an increased prevalence of cancer, including hematological malignancies and solid tumors8,9,10. Lung cancer is one of the most common pSS-related cancers, especially lung adenocarcinoma (LUAD)11.
Collectively, further investigation suggested that pSS with LUAD may have some underlying common pathogenesis. According to our current knowledge, no special studies yet explain the common mechanisms between the two diseases. Recently, bioinformatics analysis offer a potential possibility for us to reveal potentially shared disease mechanisms across species12,13,14. To further reveal the underlying mechanisms, bioinformatics analysis is used for the analysis of common targets and signaling pathways between pSS and LUAD, and animal models are subsequently established for experimental verification. Revealing these mechanisms may help provide an evidence base for clinical prevention of LUAD in pSS patients.
This study used the GEO and Genecard databases to retrieve the relevant genes associated with pSS and LUAD. Afterward, DEGs associated with pSS and LUAD were screened as pSS-LUAD-DEGs. We performed KEGG and GO enrichment analyses to elucidate the significant biological functions of pSS-LUAD-DEGs. PPI network construction was used to identify core targets, and we further assessed hub gene diagnostic accuracy through ROC curve analyses. We used NOD/Ltj mice as pSS animal models stimulated with particulate matter 2.5 (PM2.5) to generate an inflammatory reaction. QPCR, ELISA, and western blotting were performed to verify the study experimentally. Overall, the results here indicate that carcinogenesis induced by the pulmonary inflammatory microenvironment may be a critical reason for the high incidence of LUAD in pSS patients. It also suggests that the occurrence of LUAD in pSS patients may be prevented by blocking-related mechanisms.

<table><tbody><tr><td>3-Color Prestained Protein Marker</td><td>Epizyme</td><td>WJ103</td><td>Western Blot</td></tr><tr><td>Antibody Dilution Buffer</td><td>Epizyme</td><td>PS119</td><td>Western Blot</td></tr><tr><td>BCA Protein Quantification Kit</td><td>Epizyme</td><td>ZJ101</td><td>Western Blot</td></tr><tr><td>Cytoscape 3.7.1 software</td><td>National Institute of General Medical Sciences (NIGMS), National Institutes of Health (NIH)</td><td>Version 3.7.1.</td><td>Open-source software for biological network analysis and visualization</td></tr><tr><td>ECL Luminous Fluid</td><td>Epizyme</td><td>SQ203</td><td>Western Blot</td></tr><tr><td>Electrophoresis Buffer</td><td>Epizyme</td><td>PS105S</td><td>Western Blot</td></tr><tr><td>GraphPad Prism 10.0</td><td>GraphPad</td><td>Version 10.0</td><td>Data analysis</td></tr><tr><td>HRP-conjugated Goat anti-Rabbit IgG (H+L) (AS014)</td><td>abclonal</td><td>AS014</td><td>Western Blot</td></tr><tr><td>JAK2 Antibody</td><td>Cell Signaling Technology</td><td>3230T</td><td>Western Blot</td></tr><tr><td>Mouse IL-1&amp;beta; ELISA Kit</td><td>Beijing&amp;nbsp;4A&amp;nbsp;Biotech Co., Ltd</td><td>CME0015</td><td>ELISA</td></tr><tr><td>Mouse IL-6 ELISA Kit</td><td>Beijing&amp;nbsp;4A&amp;nbsp;Biotech Co., Ltd</td><td>CME0006</td><td>ELISA</td></tr><tr><td>Phosphatase Inhibitor Cocktail (100&amp;times;)</td><td>Epizyme</td><td>GRF102</td><td>Western Blot</td></tr><tr><td>Phospho-JAK2 (Tyr1007/1008) Antibody</td><td>Cell Signaling Technology</td><td>3776S</td><td>Western Blot</td></tr><tr><td>Phospho-STAT3 (Tyr705) Antibody</td><td>Cell Signaling Technology</td><td>9145S</td><td>Western Blot</td></tr><tr><td>Protease Inhibitor Cocktail (100&amp;times;)</td><td>Epizyme</td><td>GRF101</td><td>Western Blot</td></tr><tr><td>Protein Free Rapid Blocking Buffer (5&amp;times;)</td><td>Epizyme</td><td>PS108</td><td>Western Blot</td></tr><tr><td>PVDF membrane</td><td>Millipore</td><td>IPVH00010</td><td>Western Blot</td></tr><tr><td>R software</td><td>R Foundation for Statistical Computing</td><td>Not Applicable</td><td>Statistical analysis software and programming language used for data analysis, visualization, and machine learning applications</td></tr><tr><td>Radio Immunoprecipitation Assay</td><td>Epizyme</td><td>PC101</td><td>Western Blot</td></tr><tr><td>Reverse Transcription System</td><td>Promega</td><td>A3500</td><td>PCR</td></tr><tr><td>SDS-PAGE</td><td>Epizyme</td><td>LK303</td><td>Western Blot</td></tr><tr><td>SDS-PAGE Protein Loading Buffer (5&amp;times;)</td><td>Epizyme</td><td>LT103</td><td>Western Blot</td></tr><tr><td>STAT3 Antibody</td><td>Cell Signaling Technology</td><td>9139S</td><td>Western Blot</td></tr><tr><td>SYBR Green Realtime PCR Master Mix</td><td>TOYOBO</td><td>QPK-201</td><td>PCR</td></tr><tr><td>TBST (10&amp;times;)</td><td>Epizyme</td><td>PS103</td><td>Western Blot</td></tr><tr><td>Western Blot Transfer Buffer (10&amp;times;)</td><td>Epizyme</td><td>PS109</td><td>Western Blot</td></tr><tr><td>&amp;beta;-Actin Antibody</td><td>abclonal</td><td>AC026</td><td>Western Blot</td></tr></tbody></table>

primary sjogren's syndrome associated with lung adenocarcinoma: probing the potential common pathogenic mechanisms and experimental verification

Primary Sjogren's Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification

This study aimed to probe the potential common pathogenic mechanisms linking primary Sjogren's syndrome (pSS) and lung adenocarcinoma (LUAD) through bioinformatics analysis and experimental verification. The relevant genes associated with pSS and LUAD were retrieved from the Gene Expression Omnibus (GEO) database and Genecard database. Subsequently, differentially expressed genes (DEGs) associated with pSS and LUAD were screened as pSS-LUAD-DEGs. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed to elucidate the significant biological functions of pSS-LUAD-DEGs. Core targets were identified by constructing the protein-protein interaction (PPI) network, further assessing hub gene diagnostic accuracy through Receiver Operating Characteristic (ROC) curve analyses. In this study, NOD/Ltj mice served as pSS animal models and were stimulated with particulate matter 2.5 (PM2.5) to generate an inflammatory reaction. Quantitative real-time polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA), and western blotting were employed for relevant molecular biology experiment verification. The results revealed through KEGG and GO enrichment analyses indicate that inflammation plays a critical role in linking pSS and LUAD. IL6, CCNA2, JAK2, IL1B, ASPM, CCNB2, NUSAP1, and CEP55 were determined as key targets of pSS-LUAD. BALB/c mice and NOD/Ltj mice exhibited enhanced expression of inflammatory cytokines IL-6 and IL-1&#946; in lung tissues following 21 days of stimulation with PM2.5, activating the JAK2/STAT3 signaling pathway and up-regulating the expression of tumor-associated genes CCNA2, CCNB2, and CEP55, with NOD/Ltj mice exhibiting more pronounced changes than BALB/c mice. This protocol demonstrates that carcinogenesis induced by the pulmonary inflammatory microenvironment may be a key reason for the high incidence of LUAD in pSS patients. Additionally, blocking-related mechanisms may help prevent the occurrence of LUAD in pSS patients.

A total of 3290 DEGs were identified from the 23348 genes in GSE84884 (pSS), including 2659 up-regulated genes and 631 down-regulated genes (Figure 1A). For GSE51092 (pSS), a total of 3290 DEGs were identified from the 11409 genes, including 667 up-regulated genes and 587 down-regulated genes (Figure 1B). The GeneCards database obtained 102 ovarian pSS-related DEGs, and the correlation score of screening criteria was ≥20. The union and deduplication of the pSS-related DEGs obtained from GEO and GeneCards resulted in 453 DEGs (pSS-DEGs). A total of 7154 DEGs were identified from the 25440 genes in GSE32863 (LUAD), including 3934 up-regulated genes and 3220 down-regulated genes (Figure 1C). In GSE75037 (LUAD), a total of 11235 DEGs were identified from the 25440 genes, including 6125 up-regulated genes and 5110 down-regulated genes (Figure 1D). The GeneCards database obtained 600 ovarian LUAD-related DEGs, and the correlation score of screening criteria was ≥20. The union and deduplication of the LUAD-related DEGs obtained from GEO and GeneCards resulted in 7478 DEGs (LUAD-DEGs). A total of 233 shared DEGs (pSS-LUAD-DEGs) were selected between pSS-DEGs and LUAD-DEGs and visualized using Venn diagrams (Supplementary Table 1 and Figure 1E).
Regarding the KEGG pathway enrichment analysis, the top 10 significant signaling pathways of pSS-LUAD-DEGs identified were related to metabolic pathways after removing the disease-related signaling pathways. Specifically, these pathways include the PI3K-Akt signaling pathway, MAPK signaling pathway, Cytokine-cytokine receptor interaction, Necroptosis, Regulation of actin cytoskeleton, FoxO signaling pathway, NOD-like receptor signaling pathway, Cellular senescence, Th17 cell differentiation, and JAK-STAT signaling pathway (Figure 2A). The top 10 KEGG signaling pathways and pSS-LUAD-DEGs were visualized, as shown in Figure 2B.
The GO enrichment analysis of pSS-LUAD-DEGs revealed the following top 10 biological processes (Figure 3), and were enriched in cell component (CC): response to virus, innate immune response, defense response to virus, defense response to symbiont, regulation of viral process, regulation of viral life cycle, negative regulation of viral process, negative regulation of viral genome replication, regulation of viral genome replication, antiviral innate immune response; biological process (BP): side of membrane, external side of plasma membrane, extracellular matrix, external encapsulating structure, collagen-containing extracellular matrix, collagen trimer, membrane raft, membrane microdomain, plasma membrane raft, caveola; molecular function (MF): cytokine receptor binding, cytokine activity, signaling receptor regulator activity, tumor necrosis factor receptor superfamily binding, signaling receptor activator activity, tumor necrosis factor receptor binding, receptor ligand activity, protein homodimerization activity, transmembrane receptor protein kinase activity, protein kinase activity.
The PPI network comprised 99 nodes and 466 edges (Figure 4A). The top 20 genes with higher degrees in the PPI network are STAT3, STAT1, TP53, TNF, IL6, IFNG, EGFR, ISG15, CCNA2, IL10, JAK2, MX1, IL1B, IFIT1, AKT1, SMAD3, ASPM, CCNB2, NUSAP1 and CEP55, serving as hub genes (Figure 4B).
ROC curve analysis was performed to evaluate the diagnostic accuracy of the 20 hub genes (Figure 5). The area under the curve (AUC) values for the ROC curves of STAT3, STAT1, TP53, TNF, IL6, IFNG, EGFR, ISG15, CCNA2, IL10, JAK2, MX1, IL1B, IFIT1, AKT1, SMAD3, ASPM, CCNB2, NUSAP1, and CEP55 were 0.543, 0.840, 0.724, 0.892, 0.965, 0.529, 0.721, 0.763, 0.933, 0.784, 0.836, 0.648, 0.936, 0.689, 0.662, 0.548, 0.960, 0.939, 0.934, and 0.953 for GSE32863 (Figure 5A,D), and 0.620, 0.811, 0.676, 0.878, 0.955, 0.546, 0.642, 0.765, 0.945, 0.759, 0.804, 0.625, 0.938, 0.753, 0.641, 0.540, 0.958, 0.945, 0.908, and 0.941 for GSE75037 (Figure 5B,E). Additionally, for the validation set GSE31210 (Figure 5C,F), the AUC values were 0.505, 0.676, 0.819, 0.688, 0.863, 0.540, 0.720, 0.665, 0.814, 0.562, 0.766, 0.661, 0.806, 0.528, 0.607, 0.652, 0.906, 0.927, 0.910, and 0.898. The AUC values for IL6, CCNA2, JAK2, IL1B, ASPM, CCNB2, NUSAP1, and CEP55 were &gt;0.7, suggesting that all eight have diagnostic significance.
In lung tissues of BALB/c mice and NOD/Ltj mice, only a small amount of pro-inflammatory factors IL-6 and IL-1β expression was detected. Following stimulation with PM2.5, the expression of IL-6 and IL-1β significantly increased, with a notably higher elevation observed in NOD/Ltj mice compared to BALB/c mice (Figure 6).
Following PM2.5 stimulation, the expression levels of JAK2/STAT3 did not change significantly in the lung tissues of BALB/c mice and NOD/Ltj mice. However, the levels of p-JAK2 and p-STAT3 increased significantly. NOD/Ltj mice showed significantly higher expression of p-JAK2 and p-STAT3 compared to BALB/c mice following PM2.5 stimulation (Figure 7).
Following PM2.5 stimulation, mRNA expression of ASPM and NUSAP1 did not change significantly in both BALB/c mice and NOD/Ltj mice, but the mRNA expression of CCNA2, CCNB2 and CEP55 increased (Figure 8). Notably, the mRNA expression of CCNA2, CCNB2, and CEP55 was significantly higher in NOD/Ltj mice compared to BALB/c mice following PM2.5 stimulation.
<img alt="Volcano plots A-D and Venn diagram E displaying gene expression analysis results and overlap." class="xfigimg" src="/files/ftp_upload/67421/67421fig01.jpg" data-altupdatedat="1788304762000" data-alt="Figure 1"> 
Figure 1: The volcano map and Venn diagram of DEGs. (A) The volcano map of GSE84844.(B) The volcano map of GSE51092. (C) The volcano map of GSE32863. (D) The volcano map of GSE75037. (E) Venn diagram of pSS-DEGs and LUAD-DEGs. Up-regulated genes are colored in red; down-regulated genes are colored in green. Abbreviations: pSS = primary Sjögren's Syndrome; LUAD = lung adenocarcinoma; DEGs = differentially expressed genes. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Gene pathway analysis; scatter plot of gene ratio vs. pathway and network diagram; data analysis." class="xfigimg" src="/files/ftp_upload/67421/67421fig02.jpg" data-altupdatedat="1788304762000" data-alt="Figure 2"> 
Figure 2: KEGG pathway enrichment of pSS-LUAD-DEGs. (A) TOP10 KEGG enrichment map of pSS-LUAD-DEGs. (B) The association between TOP10 KEGG signaling pathways with pSS-LUAD-DEGs. Abbreviations: KEGG = Kyoto Encyclopedia of Genes and Genomes; pSS = primary Sjögren's Syndrome; LUAD = lung adenocarcinoma; DEGs = differentially expressed genes. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Bar chart of gene ontology analysis; enrichment scores by biological process, cellular component, function." class="xfigimg" src="/files/ftp_upload/67421/67421fig03.jpg" data-altupdatedat="1788304762000" data-alt="Figure 3"> 
Figure 3: GO enrichment analysis of pSS-LUAD-DEGs. Abbreviations: GO = Gene Ontology; pSS = primary Sjögren's Syndrome; LUAD = Lung adenocarcinoma; DEGs = differentially expressed genes. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Gene interaction network diagram and degree centrality bar chart showing key node relationships." class="xfigimg" src="/files/ftp_upload/67421/67421fig04.jpg" data-altupdatedat="1788304762000" data-alt="Figure 4"> 
Figure 4: PPI network of pSS-LUAD-DEGs. (A) Visualization of the PPI network. Redder shades indicate stronger connectivity of the node. (B) The top 20 genes with higher degrees in the PPI network. Abbreviations: PPI = protein-protein interaction; pSS = primary Sjögren's Syndrome; LUAD = lung adenocarcinoma; DEGs = differentially expressed genes. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="ROC curves comparing sensitivity vs. 1-specificity (AUC) for various biomarkers, showing performance in classification tasks, diagram." class="xfigimg" src="/files/ftp_upload/67421/67421fig05.jpg" data-altupdatedat="1788304762000" data-alt="Figure 5"> 
Figure 5: ROC curve analysis of hub genes. (A-F) ROC curve analysis of hub genes in GSE32863, GSE75037, and GSE31210. Abbreviations: ROC = Receiver Operating Characteristic. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="IL-6 and IL-1β level comparison; bar chart; experimental results; inflammation markers analysis." class="xfigimg" src="/files/ftp_upload/67421/67421fig06.jpg" data-altupdatedat="1788304762000" data-alt="Figure 6"> 
Figure 6: The impact of PM2.5 on the pro-inflammatory factors in mouse lung tissues. (A) Expression of IL-6 in mouse lung tissues of each group.(B) Expression of IL-1β in mouse lung tissues of each group. a. blank control group; b. pSS group; c. PM2.5 group; d. pSS-PM2.5 group. * p &lt; 0.05 compared with blank control group; # p &lt; 0.05 compared with pSS group; Δ p &lt; 0.05 compared with PM2.5 group. Abbreviations: PM2.5 = particulate matter 2.5. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Western blot analysis of p-JAK2, JAK2, p-STAT3, STAT3, and β-actin with graph results; protein expression." class="xfigimg" src="/files/ftp_upload/67421/67421fig07.jpg" data-altupdatedat="1788304762000" data-alt="Figure 7"> 
Figure 7: The impact of PM2.5 on the JAK2/STAT3 signaling pathway in mouse lung tissues. (A) Expression of p-JAK2, JAK2, p-STAT3, STAT3, and β-actin in mouse lung tissues of each group. (B) The ratio of p-JAK2/JAK2 in mouse lung tissues of each group. (C) The ratio of p-STAT3/STAT3 in mouse lung tissues of each group. a. blank control group; b. pSS group; c. PM2.5 group; d. pSS-PM2.5 group. * p &lt; 0.05 compared with blank control group; # p &lt; 0.05 compared with pSS group; Δ p &lt; 0.05 compared with PM2.5 group. Abbreviations: PM2.5 = particulate matter 2.5. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Bar graph measuring CD163, CD206, Arg1, NLRP3, GSDES via actin; comparative data analysis." class="xfigimg" src="/files/ftp_upload/67421/67421fig08.jpg" data-altupdatedat="1788304762000" data-alt="Figure 8"> 
Figure 8: The impact of PM2.5 on LUAD-related genes in mouse lung tissues. (A) Expression of CCNB2 mRNA in mouse lung tissues of each group. (B) Expression of CCNA2 mRNA in mouse lung tissues of each group. (C) Expression of ASPM mRNA in mouse lung tissues of each group. (D) Expression of NUSAP1 mRNA in mouse lung tissues of each group. (E) Expression of CEP55 mRNA in mouse lung tissues of each group. a. blank control group; b. pSS group; c. PM2.5 group; d. pSS-PM2.5 group. * p &lt; 0.05 compared with blank control group; # p &lt; 0.05 compared with pSS group; Δ p &lt; 0.05 compared with PM2.5 group. Abbreviations: PM2.5 = particulate matter 2.5. <a href="https://www.jove.com/files/ftp_upload/67421/67421fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Table 1: List of the shared DEGs (pSS-LUAD-DEGs) selected between pSS-DEGs and LUAD-DEGs. <a href="https://www.jove.com/files/ftp_upload/67421/Supplementary%20Table%201.zip" target="_blank">Please click here to download this File.</a>
Supplementary Coding File 1: R code for filtering DEGs. <a href="https://www.jove.com/files/ftp_upload/67421/DEGs_R_code.zip" target="_blank">Please click here to download this File.</a>

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.

This study aimed to probe the potential common pathogenic mechanisms linking primary Sjogren's syndrome (pSS) and lung adenocarcinoma (LUAD) through ...

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Orthotopic Transplantation of Syngeneic Lung Adenocarcinoma Cells to Study PD-L1 Expression

Salivary gland ultrasonography is a promising tool for the diagnosis and evaluation of disease activity and complications in Sjogren's syndrome (SS). The scanning technique for salivary gland ultrasound examination relevant to SS is described in the article.

Ultrasonographic Evaluation of Salivary Glands for Sjogren's Syndrome: Diagnostic and Monitoring Insights

Here, we present a protocol for the examination of oral Candida infection in patients with primary Sjögren's syndrome, which can be used for timely treatment and, thereafter, avoiding related complications.

Examination of Oral Candida Infection in Primary Sj&#246;gren&#39;s Syndrome Patients

Experimental human pneumococcal carriage offers a natural model of carriage and a potential model for use in vaccine development. This technique is valuable yet complex and involves clinical risk by introducing a pathogen into a human. We have developed a detailed protocol.

Experimental Human Pneumococcal Carriage

The goal of this protocol is to study breast tumorigenesis. With this technique, mouse mammary tumors are removed and primary cells are prepared from tumors. A lung extraction protocol is included for studying lung metastasis. Furthermore, another protocol for analyzing mouse embryonic fibroblasts from the mouse embryo is included.

Primary Tumor and MEF Cell Isolation to Study Lung Metastasis

Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Presented here is a protocol for sampling of human placental villous tissue followed by isolation of cytotrophoblasts for primary cell culture. Treatment of trophoblasts with TNFα recapitulates inflammation in the obese intrauterine environment and facilitates the discovery of molecular targets regulated by inflammation in placentas with maternal obesity.

A Primary Human Trophoblast Model to Study the Effect of Inflammation Associated with Maternal Obesity on Regulation of Autophagy in the Placenta

We describe methods to develop an experimental model of diet-induced metabolic syndrome (MetS) in rabbits using a high-fat, high-sucrose diet. Animals developed central obesity, mild hypertension, pre-diabetes, and dyslipidemia, thus reproducing the main components of human MetS. This chronic model will allow acquisition of knowledge underlying mechanisms of disease progression.

An Experimental Model of Diet-Induced Metabolic Syndrome in Rabbit: Methodological Considerations, Development, and Assessment

Explore bioinformatics analysis linking primary Sjogren's syndrome and lung adenocarcinoma through gene expression profiling and experimental verification. This computational approach reveals shared pathogenic mechanisms.

Primary Sjogrens Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification (Video) | JoVE Journal

Primary Sjogren's Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification

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