Method Article

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

1.1K views

⸱

DOI:

10.3791/67421

⸱

September 20th, 2024

 ,  ,  ,  ,  , 
1Graduate School, Beijing University of Chinese Medicine, 2Oncology Department, Wangjing Hospital, China Academy of Chinese Medical Sciences, 3The Second Department of Healthcare, China-Japan Friendship Hospital, 4Rheumatology Department, Shangqiu Hospital of Traditional Chinese Medicine

In This Article

Summary

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.

Abstract

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-1β 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.

Introduction

Primary Sjögren'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.

Protocol

The experimental animals were housed in the animal facility of the China-Japan Friendship Hospital, where the housing conditions met the animal feeding environment in line with China's national standard, Laboratory Animal-Requirements of Environment and Housing Facilities (GB14925-2010). All animal care procedures and experiments complied with the ARRIVE guidelines and were based on the 3R principles (reduction, replacement, refinement), adhering to the guidelines of the National Animal Welfare Law of China. BALB/c mice were purchased from SPF (Beijing) Biotechnology Co., Ltd., and NOD/Ltj mice were purchased from Huafukang (Beijing) Biotechnology Co., Ltd.

1. Bioinformatics analysis

  1. Preparation of datasets
    1. Open the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo)15, and use primary Sjögren's syndrome and lung adenocarcinoma as keywords to search gene expression profiles. Then 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 their corresponding platform information.
    2. Open the Genecard database (https://www.genecards.org/)16, and use primary Sjögren's syndrome and lung adenocarcinoma as keywords to obtain the genes of pSS and LUAD. Download the spreadsheets of the disease genes.
  2. Identification of shared DEGs between pSS and LUAD
    1. Download and open the R software (https://cran.r-project.org/)​17. Install the GEOquery R package, stringr R package, ggplot2 R package, reshape2 R package, and limma R package in R software. 
    2. Identify and visualize differentially expressed genes (DEGs) in different GEO datasets (GSE84844, GSE51092, GSE32863, and GSE75037) using R software, then compare and analyze gene expression among these datasets. Consider the genes with an adjusted P-value < 0.05 and fold change (FC) > 1.2 or < 0.83 as DEGs.
    3. Select genes with an expression level greater than or equal to 20 related to pSS and LUAD from the Genecard database.
    4. Merge the DEGs associated with pSS and the DEGs associated with LUAD from both the GEO database and the Genecard database.
    5. Install and load the VennDiagram package in R to obtain and visualize the DEGs associated with pSS and LUAD (pSS-LUAD-DEGs).
  3. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis
    1. Enter Metascape (https://metascape.org/)18. Click Select File and upload the .xlsx format file of pSS-LUAD-DEGs. Select H. sapiens in Input as Species. Similarly, select H. sapiens in Analysis as Species.
    2. Click Custom Analysis. Click Enrichment and select KEGG Pathway. Click Enrichment Analysis, then click Analysis Report Page when the enrichment analysis is complete.
    3. Click All in One Zip File to download the result. Access the _ FINAL_GO.csv file in the download Enrichment_GO folder to view the result.
    4. Use the ggplot2 package to perform the KEGG visualization program in R.
  4. Gene Ontology (GO) enrichment analysis
    1. Install and load clusterProfiler and enrichplot package in R.
    2. Import the text-format list of pSS-LUAD-DEGs into R.
    3. Run the clusterProfiler and enrichplot packages for GO enrichment analysis and result visualization. Define statistical significance in the analysis at an adjusted P-value < 0.05.
  5. Construction of protein-protein interaction (PPI) network and module analysis
    1. Enter the Retrieval of Interacting Genes (STRING) database (http://string-db.org/)19. Click Browse and upload the file of pSS-LUAD-DEGs. Select Homo sapiens in Organisms, then click Search.
    2. Click Continue. Once the results are available, click on Settings. Under Basic Settings > Minimum Required Interaction Score, select High Confidence (0.700). Tick Hide Disconnected Nodes in the Network in Advanced Settings, then click Update.
    3. Click on Exports in the title bar to download the PPI relationship text in TSV format.
    4. Download and turn on Cytoscape 3.7.1 software (https://cytoscape.org/)20. Click  File > Import > Network from File to import the TSV format file for constructing the PPI network.
    5. Use the Network Analyzer tool to analyze the topological parameters in the network. Optimize node size and color via the style bar in the left control panel.
    6. On the menu bar, select Tools > Analyze Network. On the Table panel, click on Degree to sort components by degree in descending order. Take the top 20 genes with higher degrees as hub genes.
    7. Install and load the igraph and ggplot2 packages in R to visualize hub genes by degree.
  6. Identification and validation of hub genes
    1. Install and load the pROC package in R.
    2. Import the text-format list of hub genes into R.
    3. Plot receiver operating characteristic (ROC) curves of hub genes and calculate the area under the ROC curve (AUC) values.
      NOTE: Refer to Supplementary Coding File 1 for the R code for filtering DEGs.

2. Experimental verification

NOTE: Refer to the Table of Materials for details on the materials, reagents, and instruments used in this protocol.

  1. Animal preparation
    1. Feed 12 nine-week-old female BALB/c mice and 12 nine-week-old female NOD/Ltj mice adaptively for 1 week.
    2. Use a random number table to evenly allocate 12 nine-week-old female BALB/c mice into the blank control group and the PM2.5 group. Similarly, evenly divide 12 nine-week-old female NOD/Ltj mice into the pSS group and the pSS-PM2.5 group.
  2. Preparation of the particulate matter 2.5 (PM2.5) suspension
    NOTE: PM2.5 can induce the occurrence and development of inflammation-related LUAD in mouse model21. BALB/c mice and NOD/Ltj mice were induced by PM2.5 to develop inflammation-related changes. According to the method described by Piao et al.22, the concentration of the PM2.5 suspension was prepared at 1 mg/mL.
    1. Weigh the PM2.5 quartz fiber membranes, cut them into 2cm × 2 cm pieces, and immerse them in a beaker containing an appropriate amount of deionized water.
    2. Seal the beaker and sonicate it in a water bath sonicator at 37 °C for 30 min each time. Repeat this process 3 times until the particles are completely dissolved.
    3. Filter the liquid in the beaker through 16 layers of sterile medical gauze, then squeeze out any residual moisture from the gauze.
    4. Place the filtrate on a flat dish and freeze it into ice blocks in a -20 °C freezer. Then, use a freeze-drying instrument (−52 °C, 0.1 mbar, 48 h) to dry the ice blocks of filtrate and collect the powdered particles.
    5. Dissolve the powdered particles thoroughly in saline to prepare a PM2.5 suspension with a concentration of 1 mg/mL. Pour the PM2.5 suspension into a glass bottle, place it in a high-pressure sterilizer, and apply high pressure and temperature (15 min at 121°C, 15 psi) for sterilization.
    6. Perform ultrasonic agitation (200 W, 10 s of agitation, 10 s of rest, for 3 cycles). After treatment, store it in a 4 °C refrigerator for later use.
  3. Establishment of the PM2.5 mouse model
    NOTE: The PM2.5 group and the pSS-PM2.5 group were administered PM2.5 suspension by trachea drip once every 3 days for 28 days, with each dose being 0.1 mL. The blank control group and the pSS group were administered physiological saline by trachea drip once every 3 days for 28 days, with each dose being 0.1 mL.
    1. Inject a Ketamine (100 mg/kg) and Xylazine (10 mg/kg) mixture into the mouse. Confirm the surgical plane of anesthesia by checking for a lack of response to a toe pinch and ensuring complete muscle relaxation. Continuously monitor reflexes, breathing, and overall response to ensure adequate anesthesia is maintained throughout the procedure until all steps are completed.
    2. Position the mouse on the small animal restrainer with its abdomen facing upwards, head elevated, and tail lowered at a 45° angle. Use fine thread to loop around the upper incisors of the mouse, pulling them upwards, and secure the thread onto a screw on the animal holder, ensuring full exposure of the mouse's oral cavity.
    3. Open the cold light lamp and shine the light onto the skin of the mouse's neck. Use forceps to pull out the mouse's tongue, fully exposing the glottis. Observe within the mouse's oral cavity a recurring opening and closing spot of light, which indicates the position of the mouse's airway opening.
    4. Insert the 18 G venous indwelling needle into the mouse trachea, pull out the needle core, place the cotton thread at the outer end of the venous indwelling needle, and confirm success when observing the cotton thread moving with the mouse's chest movements.
    5. Aspirate 0.2 mL of air first using a 1 mL syringe, then aspirate 0.1 mL of PM2.5 suspension, followed by aspirating another 0.2 mL of air. Inject the mixture through the 18 G venous indwelling needle into the trachea.
    6. Pull out the 18 G venous indwelling needle. Secure the animal holder upright and rotate it clockwise and counterclockwise 30 times to evenly distribute the PM2.5 suspension in the mouse's lungs. Then, extend the mouse's neck straight and lay it on its side to prevent suffocation.
  4. Collection of specimens
    NOTE: Collect specimens on the 29th day of the experiment for subsequent molecular biology analyses.
    1. Check the connection of the euthanasia system and turn on the controller power. Open the carbon dioxide (CO2) cylinder valve.
      NOTE: Do not pre-fill the euthanasia chamber before placing the animals inside.
    2. Place the mice into the chamber and infuse CO2 at 30%-70% of the chamber volume per minute. Expose the mice to CO2 for 5 min, ensuring they are immobile, not breathing, and have dilated pupils. Turn off the CO2 cylinder valve and observe for an additional 5 min to confirm death.
    3. Place the euthanized mouse in a supine position on a clean dissecting board. Expose the trachea, heart, and lungs.
    4. Use scissors and forceps to remove the skin and muscles covering the ventral thoracic and neck regions. Use scissors and forceps to make incisions along the edges of the ribs on both sides of the chest cavity to expose the thoracic cavity containing the heart and lungs. Then, cut the clavicle to create a wide enough opening to thoroughly examine the left and right lung lobes.
    5. 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.
    6. Grasp the trachea near the jaw with forceps and make a complete transverse incision using scissors placed above the forceps.
    7. Gently pull up the trachea with forceps, cutting ventral tissue connections with scissors until the entire thoracic tissue is removed from the body.
    8. Lay the lungs flat on the workbench. Rinse lung tissue surface residue with saline, blot dry with filter paper, aliquot into cryotubes, and store at -80 °C.
  5. Quantitative real-time polymerase chain reaction (qPCR)
    1. Ground the lung tissue into powder using a mortar containing liquid nitrogen.
    2. Weigh 20 mg of ground tissue using a precision balance, combine it with 750 µL of Buffer RL in a centrifuge tube, mix thoroughly using a vortex mixer, and let it stand at room temperature (RT) for 3 min. Then, centrifuge it at 14,000 x g for 5 min at RT and collect the supernatant.
    3. Place the genomic DNA (gDNA) filter mini column into a 2 mL collection tube. Transfer the supernatant to the gDNA filter mini column and centrifuge at 14,000 x g for 2 min at RT.
    4. Discard the gDNA filter mini column. Add an equal volume of 70% ethanol to the filtrate and mix by pipetting up and down 5 times.
    5. Place the RNA mini column into a 2 mL collection tube. Transfer 750 µL of the mixture to the RNA mini column and centrifuge at 12,000 x g for 1 min at RT.
    6. Discard the filtrate and place the RNA mini column back into the 2 mL collection tube. Add 500 µL of Buffer RW1 to the RNA mini column and centrifuge at 12,000 x g for 1 min at RT.
    7. Discard the filtrate and place the RNA mini column back into the 2 mL collection tube. Add 500 µL of Buffer RW2 to the RNA mini column. Centrifuge at 12,000 x g for 1 min at RT. Repeat this step once more.
    8. Discard the filtrate and place the RNA mini column back into the 2 mL collection tube. Centrifuge at 12,000 x g for 2 min at RT.
    9. Transfer the RNA mini column to a 1.5 mL centrifuge tube, add 100 µL of RNase-free water to the center of the column membrane, and incubate at RT for 2 min. Then, centrifuge at 12,000 x g for 1 min at RT. Discard the RNA mini column and store the RNA solution at -80 °C.
    10. Take 2 µL of the RNA solution and measure it using the NanoDrop spectrophotometer according to the equipment manual to determine its concentration and quality.
      NOTE: The study selected 260/280 and 260/230 ratios for RNA quality control.
    11. Prepare the RNA solution by sequentially adding the following components into a microcentrifuge tube: 1 µg of total RNA, 4 µL of MgCl2 (25 mM), 2 µL of reverse transcription 10x buffer, 2 µL of dNTP mixture (10 mM), 0.5 µL of recombinant ribonuclease inhibitor, 15 units of reverse transcriptase, 0.5 µg of oligo(dT)15 primer, and add Nuclease-Free Water to 20 µL. Mix the contents gently to collect all the liquid at the bottom of the tube.
      NOTE: Cocktail solutions must be prepared to ensure more consistent cDNA synthesis and RNA quantification.
    12. Reverse-transcribe the 20 µL RNA solution into cDNA by incubating it at 42 °C for 15 min, denaturing it at 95 °C for 5 min, and then cooling it to 4 °C, followed by storage at -20 °C.
    13. Combine and thoroughly mix 6.4 µL of distilled water, 10 µL of SYBR Green real-time PCR master mix, 2 µL of the obtained cDNA solution, 0.8 µL of forward primer (10 µM), and 0.8 µL of reverse primer (10 µM) (Table 1) to prepare the reaction system.
    14. Run the reaction on the PCR instrument to obtain the cycle threshold (CT) values for the target genes and reference genes.
      1. Set the initial denaturation at 95 °C for 60 s at the beginning of each PCR cycle.
      2. During the PCR cycles, perform denaturation at 95 °C for 15 s, annealing at 60 °C for 15 s, and extension at 72 °C for 45 s, collecting data during the extension step. Conduct a total of 40 PCR cycles.
      3. After completing the PCR cycles, perform melting curve analysis automatically using the PCR instrument.
        NOTE: If the melting curve shows a double peak or irregular peak shape, this indicates that there may be an issue with the experiment.
    15. Determine the relative expression levels of each target gene using the 2-ΔΔCT method, followed by further statistical analysis. Use the following list of formulas for the 2-ΔΔCT method:
      ΔCT (test) = CT (target, test) - CT (ref, test)
      ΔCT (calibrator) = CT (target, calibrator) - CT (ref, calibrator)
      ΔΔCT = ΔCT (test) -ΔCT (calibrator)
      2-ΔΔCT = Fold change in gene expression

Gene nameSequence (5′ to 3′)
Mouse CCNA2 forwardCCCAGAAGTAGCAGAGTTTGTG
Mouse CCNA2 reverseTTGTCCCGTGACTGTGTAGAG
Mouse ASPM forwardCTTATTCAGGCTATGTGGAGGA
Mouse ASPM reverseCCAGGCTTGAATCTTGCAG
Mouse CCNB2 forwardTTGAAATTTGAGTTGGGTCGAC
Mouse CCNB2 reverseCTGTTCAACATCAACCTCCC
Mouse NUSAP1 forwardCTCCCTCAAGTACAGTGACC
Mouse NUSAP1 reverseTTTAACAACTTGGTTGCCCTC
Mouse CEP55 forwardCCGCCAGAATATGCAGCATCAAC
Mouse CEP55 reverseAGTGGGAATGGCTGCTCTGTGA

Table 1: Prime sequences for quantitative real-time PCR.

  1. Enzyme-linked immunosorbent assay (ELISA)
    NOTE: According to the instructions of the ELISA kit, equilibrate the ELISA kit to RT, prepare a wash buffer, and dilute the standard. Dilute 90 µL of concentrated biotinylated antibody with 8910 µL of biotinylated antibody dilution buffer to prepare a biotinylated antibody working solution (1:100) in advance. Similarly, dilute 90 µL of concentrated enzyme conjugate with 8910 µL of enzyme conjugate dilution buffer to prepare an enzyme conjugate working solution (1:100) in advance.
    1. Grind the lung tissue into powder using a mortar containing liquid nitrogen.
    2. Weigh 50 mg of lung tissue using a precision balance, combine it with 1 mL of PBS in a grinding tube, and thoroughly grind it on ice.
    3. Centrifuge the mixture at 4 °C, 3000 x g for 5 min, and collect the supernatant as a specimen.
    4. Place 100 µL of specimen/standard of different concentrations into respective wells of the ELISA plate, cover the reaction wells with adhesive sealing film, and incubate in a 37 °C incubator for 90 min.
    5. Use an automated plate washer to wash the ELISA plate 4 times, injecting 350 µL of wash buffer each time with a 30 s interval between injection and aspiration.
    6. Add 100 µL of biotinylated antibody working solution per well, seal the wells with adhesive sealing film, and incubate in a 37 °C incubator for 60 min. Afterward, wash the ELISA plate 4 times following the procedure described previously.
    7. Add 100 µL of enzyme conjugate working solution per well, seal the wells with adhesive sealing film, and incubate in a 37 °C incubator for 30 min. Afterward, wash the ELISA plate 4 times following the procedure described previously.
    8. Add 100 µL of chromogenic agent per well, protect from light, and incubate in a 37 °C incubator for 10-20 min. Then, add 100 µL of stop solution per well, mix thoroughly, and immediately measure the optical density at 450 nm (OD450) values.
      NOTE: An 8-channel pipette is used to add the biotinylated antibody working solution, enzyme conjugate working solution, and stop solution, which allows for completing the addition quickly and avoiding potential errors.
    9. Generate a standard curve using CurveExpert software (http:// curveexpert.webhop.net/) to calculate the concentration of the target substances in each sample well.
  2. Western blotting
    1. Prepare the radioimmunoprecipitation assay (RIPA) solution by mixing RIPA lysis buffer, Phenylmethanesulfonyl fluoride (PMSF), and phosphatase inhibitor at a ratio of 100:1:1 and place it on ice.
    2. Grind the lung tissues into powder using a mortar containing liquid nitrogen.
    3. Weigh 20 mg of lung tissue accurately using a precision balance and add it to 250 µL of the RIPA mixed solution.
    4. Incubate the mixture on ice for 20 min, then centrifuge it at 12,000 x g for 15 min at 4 °C, and collect the supernatant as the sample.
    5. Prepare the BCA working solution by mixing Reagent A and Reagent B from the BCA kit in a 50:1 ratio.
      1. Dilute the standard to prepare diluted standard solutions with concentrations of 0, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5 mg/mL. Add 20 µL of each standard solution and sample into separate wells of a 96-well plate and incubate at 37 °C for 30 min.
      2. Measure the absorbance at 490 nm using the microplate reader. Fit the standard curve using the concentrations and absorbances of the standard solutions, and calculate the sample concentration23. Adjust sample concentrations to the same level using the RIPA mixed solution.
    6. Mix the protein supernatant with 5x loading buffer at a ratio of 4:1 in a centrifuge tube. Boil in a metal bath for 5 min, then centrifuge it at 12,000 x g for 15 min at 4 °C and collect the supernatant.
    7. Perform protein separation using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transfer the proteins onto a polyvinylidene fluoride (PVDF) membrane electrically. Block the PVDF membrane with 5% skim milk at RT for 1 h.
    8. Incubate the PVDF membrane with primary antibody (diluted 1:1000) at 4 °C for 12 h, followed by washing three times with TBST for 10 min each, and then incubate with secondary antibody (diluted 1:5000) at RT for 2 h.
    9. Detect the target bands using a fully automatic electrochemiluminescence immunoassay system and perform quantitative analysis using the built-in software.
  3. Statistical analysis
    1. Utilize appropriate software for statistical analysis.
    2. Present experimental data as mean ± standard deviation.
    3. Determine significance using one-way analysis of variance (ANOVA).
    4. Consider P < 0.05 as statistically significant.

Results

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 >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.

Volcano plots A-D and Venn diagram E displaying gene expression analysis results and overlap.
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. Please click here to view a larger version of this figure.

Gene pathway analysis; scatter plot of gene ratio vs. pathway and network diagram; data analysis.
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. Please click here to view a larger version of this figure.

Bar chart of gene ontology analysis; enrichment scores by biological process, cellular component, function.
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. Please click here to view a larger version of this figure.

Gene interaction network diagram and degree centrality bar chart showing key node relationships.
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. Please click here to view a larger version of this figure.

ROC curves comparing sensitivity vs. 1-specificity (AUC) for various biomarkers, showing performance in classification tasks, diagram.
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. Please click here to view a larger version of this figure.

IL-6 and IL-1β level comparison; bar chart; experimental results; inflammation markers analysis.
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 < 0.05 compared with blank control group; # p < 0.05 compared with pSS group; Δ p < 0.05 compared with PM2.5 group. Abbreviations: PM2.5 = particulate matter 2.5. Please click here to view a larger version of this figure.

Western blot analysis of p-JAK2, JAK2, p-STAT3, STAT3, and β-actin with graph results; protein expression.
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 < 0.05 compared with blank control group; # p < 0.05 compared with pSS group; Δ p < 0.05 compared with PM2.5 group. Abbreviations: PM2.5 = particulate matter 2.5. Please click here to view a larger version of this figure.

Bar graph measuring CD163, CD206, Arg1, NLRP3, GSDES via actin; comparative data analysis.
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 < 0.05 compared with blank control group; # p < 0.05 compared with pSS group; Δ p < 0.05 compared with PM2.5 group. Abbreviations: PM2.5 = particulate matter 2.5. Please click here to view a larger version of this figure.

Supplementary Table 1: List of the shared DEGs (pSS-LUAD-DEGs) selected between pSS-DEGs and LUAD-DEGs. Please click here to download this File.

Supplementary Coding File 1: R code for filtering DEGs. Please click here to download this File.

Discussion

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 NFκB signaling pathway by phosphorylating IκB 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 µm. 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-1β 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-1β was significantly increased, indicating the successful establishment of the experimental model of pulmonary inflammatory microenvironment. The expression levels of IL-6 and IL-1β 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-1β, 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-1β, 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-1β monoclonal antibodies), and tofacitinib (JAK inhibitors), which are related to inflammation, may serve as potential candidate drugs for preventing LUAD in pSS patients.

Disclosures

The authors have no conflicts of interest to disclose.

Acknowledgements

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).

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
3-Color Prestained Protein MarkerEpizymeWJ103Western Blot
Antibody Dilution BufferEpizymePS119Western Blot
BCA Protein Quantification KitEpizymeZJ101Western Blot
Cytoscape 3.7.1 softwareNational Institute of General Medical Sciences (NIGMS), National Institutes of Health (NIH)Version 3.7.1.Open-source software for biological network analysis and visualization
ECL Luminous FluidEpizymeSQ203Western Blot
Electrophoresis BufferEpizymePS105SWestern Blot
GraphPad Prism 10.0GraphPadVersion 10.0Data analysis
HRP-conjugated Goat anti-Rabbit IgG (H+L) (AS014)abclonalAS014Western Blot
JAK2 AntibodyCell Signaling Technology3230TWestern Blot
Mouse IL-1β ELISA KitBeijing 4A Biotech Co., LtdCME0015ELISA
Mouse IL-6 ELISA KitBeijing 4A Biotech Co., LtdCME0006ELISA
Phosphatase Inhibitor Cocktail (100×)EpizymeGRF102Western Blot
Phospho-JAK2 (Tyr1007/1008) AntibodyCell Signaling Technology3776SWestern Blot
Phospho-STAT3 (Tyr705) AntibodyCell Signaling Technology9145SWestern Blot
Protease Inhibitor Cocktail (100×)EpizymeGRF101Western Blot
Protein Free Rapid Blocking Buffer (5×)EpizymePS108Western Blot
PVDF membraneMilliporeIPVH00010Western Blot
R softwareR Foundation for Statistical ComputingNot ApplicableStatistical analysis software and programming language used for data analysis, visualization, and machine learning applications
Radio Immunoprecipitation AssayEpizymePC101Western Blot
Reverse Transcription SystemPromegaA3500PCR
SDS-PAGEEpizymeLK303Western Blot
SDS-PAGE Protein Loading Buffer (5×)EpizymeLT103Western Blot
STAT3 AntibodyCell Signaling Technology9139SWestern Blot
SYBR Green Realtime PCR Master MixTOYOBOQPK-201PCR
TBST (10×)EpizymePS103Western Blot
Western Blot Transfer Buffer (10×)EpizymePS109Western Blot
β-Actin AntibodyabclonalAC026Western Blot

References

  1. Thorlacius, G. E., Bjork, A., Wahren-Herlenius, M. Genetics and epigenetics of primary sjogren syndrome: Implications for future therapies. Nat Rev Rheumatol. 19 (5), 288-306 (2023).
  2. Bjordal, O., Norheim, K. B., Rodahl, E., Jonsson, R., Omdal, R. Primary Sjogren's syndrome and the eye. Surv Ophthalmol. 65 (2), 119-132 (2020).
  3. Zhong, H., et al. Hyperglobulinemia predicts increased risk of mortality in primary Sjogren's syndrome: Based on a Chinese multicentre registry. Mod Rheumatol. 34 (1), 137-143 (2023).
  4. Lin, W., et al. Interstitial lung disease in primary Sjogren's syndrome. BMC Pulm Med. 22 (1), 73(2022).
  5. Aiyegbusi, O., et al. Renal disease in primary Sjogren's syndrome. Rheumatol Ther. 8 (1), 63-80 (2021).
  6. Liampas, A., et al. Primary sjogren syndrome-related peripheral neuropathy: A systematic review and meta-analysis. Eur J Neurol. 30 (1), 255-265 (2023).
  7. Wu, J., et al. Clinical and laboratory features of primary Sjogren's syndrome complicated with mild to severe thrombocytopenia. Ann Transl Med. 10 (6), 300(2022).
  8. Zhong, H., et al. Primary Sjogren's syndrome is associated with increased risk of malignancies besides lymphoma: A systematic review and meta-analysis. Autoimmun Rev. 21 (5), 103084(2022).
  9. Goulabchand, R., et al. Cancer incidence in primary Sjogren's syndrome: Data from the French hospitalization database. Autoimmun Rev. 20 (12), 102987(2021).
  10. Witkowski Durand Viel, P., et al. Chronological interplay, clinical features, and treatments among patients with cancer and primary Sjogren's syndrome. Cancer Immunol Immunother. 72 (12), 4309-4322 (2023).
  11. Xu, Y., et al. The prevalence and clinical characteristics of primary Sjogren's syndrome patients with lung cancer: An analysis of ten cases in China and literature review. Thorac Cancer. 6 (4), 475-479 (2015).
  12. Shi, L., Du, X., Li, J., Zhang, G. Bioinformatics and systems biology approach to identify the pathogenetic link between psoriasis and cardiovascular disease. Clin Cosmet Investig Dermatol. 16, 2283-2295 (2023).
  13. Xu, R., Ma, L. L., Cui, S., Chen, L., Xu, H. Bioinformatics and systems biology approach to identify the pathogenetic link between heart failure and sarcopenia. Arq Bras Cardiol. 120 (10), e20220874(2023).
  14. Lv, Y., et al. Bioinformatics and systems biology approach to identify the pathogenetic link of long COVID and myalgic encephalomyelitis/chronic fatigue syndrome. Front Immunol. 13, 952987(2022).
  15. Barrett, T., et al. Ncbi geo: Archive for functional genomics data sets--update. Nucleic Acids Res. 41 (database issue), D991-D995 (2013).
  16. Stelzer, G., et al. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 54, 1.30.1-1.30.33 (2016).
  17. Liu, S., et al. Three differential expression analysis methods for RNA sequencing: limma, EdgeR, DESeq2. J Vis Exp. 175, e62528(2021).
  18. Zhou, Y., et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 10 (1), 1523(2019).
  19. Szklarczyk, D., et al. The string database in 2023: Protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51 (D1), D638-D646 (2023).
  20. Shannon, P., et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13 (11), 2498-2504 (2003).
  21. Hill, W., et al. Lung adenocarcinoma promotion by air pollutants. Nature. 616 (7955), 159-167 (2023).
  22. Piao, C. H., et al. PM2.5 exposure regulates th1/th2/th17 cytokine production through NF-κβ signaling in combined allergic rhinitis and asthma syndrome. Int Immunopharmacol. 119, 110254(2023).
  23. Desjardins, P., Hansen, J. B., Allen, M. Microvolume protein concentration determination using the nanodrop 2000c spectrophotometer. J Vis Exp. 33, e1610(2009).
  24. Luo, J., et al. Distinct clinical phenotypes of primary Sjogren's syndrome differ by onset age: A retrospective study of 742 cases and review of the literature. Clin Exp Rheumatol. 40 (12), 2373-2380 (2022).
  25. Luppi, F., et al. Lung complications of Sjogren syndrome. Eur Respir Rev. 29 (157), (2020).
  26. Berardicurti, O., et al. Interstitial lung disease and pulmonary damage in primary Sjogren's syndrome: A systematic review and meta-analysis. J Clin Med. 12 (7), 2586(2023).
  27. Roca, F., et al. Interstitial lung disease in primary Sjogren's syndrome. Autoimmun Rev. 16 (1), 48-54 (2017).
  28. Fisher, D. A., et al. Diagnosis and treatment of lung cancer in the setting of interstitial lung disease. Radiol Clin North Am. 60 (6), 993-1002 (2022).
  29. Okudela, K., et al. Implications of thyroid transcription factor-1 gene methylation in carcinogenesis of interstitial pneumonia-related non-terminal respiratory unit lung adenocarcinoma. Int J Clin Exp Pathol. 15 (3), 120-130 (2022).
  30. Sekine, A., et al. Disease activity of lung cancer at the time of acute exacerbation of interstitial lung disease during cytotoxic chemotherapy. Thorac Cancer. 13 (17), 2443-2449 (2022).
  31. Glaviano, A., et al. PI3K/AKT/mTOR signaling transduction pathway and targeted therapies in cancer. Mol Cancer. 22 (1), 138(2023).
  32. Ediriweera, M. K., Tennekoon, K. H., Samarakoon, S. R. Role of the PI3K/AKT/mTOR signaling pathway in ovarian cancer: Biological and therapeutic significance. Semin Cancer Biol. 59, 147-160 (2019).
  33. Hoxhaj, G., Manning, B. D. The PI3K-AKT network at the interface of oncogenic signalling and cancer metabolism. Nat Rev Cancer. 20 (2), 74-88 (2020).
  34. Liu, R., et al. PI3K/AKT pathway as a key link modulates the multidrug resistance of cancers. Cell Death Dis. 11 (9), 797(2020).
  35. Manogaran, P., Beeraka, N. M., Paulraj, R. S., Sathiyachandran, P., Thammaiappa, M. Impediment of cancer by dietary plant-derived alkaloids through oxidative stress: Implications of PI3K/AKT pathway in apoptosis, autophagy, and ferroptosis. Curr Top Med Chem. 23 (10), 860-877 (2023).
  36. Acosta-Martinez, M., Cabail, M. Z. The PI3K/AKT pathway in meta-inflammation. Int J Mol Sci. 23 (23), 15330(2022).
  37. Chen, G. Y., et al. Prediction of Rhizoma Drynariae targets in the treatment of osteoarthritis based on network pharmacology and experimental verification. Evid Based Complement Alternat Med. 2021, 5233462(2021).
  38. Chen, G. Y., et al. Total flavonoids of Rhizoma Drynariae restore the MMP/TIMP balance in models of osteoarthritis by inhibiting the activation of the NF-κβ and PI3K/AKT pathways. Evid Based Complement Alternat Med. 2021, 6634837(2021).
  39. Chen, G. Y., et al. Network pharmacology analysis and experimental validation to investigate the mechanism of total flavonoids of Rhizoma Drynariae in treating rheumatoid arthritis. Drug Des Devel Ther. 16, 1743-1766 (2022).
  40. Yao, C., Narumiya, S. Prostaglandin-cytokine crosstalk in chronic inflammation. Br J Pharmacol. 176 (3), 337-354 (2019).
  41. Liu, P., et al. NOD-like receptor signaling in inflammation-associated cancers: From functions to targeted therapies. Phytomedicine. 64, 152925(2019).
  42. Damasceno, L. E. A., et al. PKM2 promotes Th17 cell differentiation and autoimmune inflammation by fine-tuning stat3 activation. J Exp Med. 217 (10), e20190613(2020).
  43. Banerjee, S., Biehl, A., Gadina, M., Hasni, S., Schwartz, D. M. JAK-STAT signaling as a target for inflammatory and autoimmune diseases: Current and future prospects. Drugs. 77 (5), 521-546 (2017).
  44. Jiramongkol, Y., Lam, E. W. FOXO transcription factor family in cancer and metastasis. Cancer Metastasis Rev. 39 (3), 681-709 (2020).
  45. Tong, X., et al. Targeting cell death pathways for cancer therapy: Recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research. J Hematol Oncol. 15 (1), 174(2022).
  46. Ou, H. L., et al. Cellular senescence in cancer: From mechanisms to detection. Mol Oncol. 15 (10), 2634-2671 (2021).
  47. Felten, R., et al. Interleukin 6 receptor inhibition in primary Sjogren syndrome: A multicentre double-blind randomised placebo-controlled trial. Ann Rheum Dis. 80 (3), 329-338 (2021).
  48. Bardsen, K., et al. Interleukin-1-related activity and hypocretin-1 in cerebrospinal fluid contribute to fatigue in primary Sjogren's syndrome. J Neuroinflammation. 16 (1), 102(2019).
  49. Barrera, M. J., et al. Tofacitinib counteracts il-6 overexpression induced by deficient autophagy: Implications in Sjogren's syndrome. Rheumatology (Oxford). 60 (4), 1951-1962 (2021).
  50. Liu, H., Zhou, Q., Xu, X., Du, Y., Wu, J. ASPM and TROAP gene expression as potential malignant tumor markers. Ann Transl Med. 10 (10), 586(2022).
  51. Qian, X., et al. CCNB2 overexpression is a poor prognostic biomarker in Chinese NSCLC patients. Biomed Pharmacother. 74, 222-227 (2015).
  52. Mo, M. L., et al. Use of serum circulating CCNB2 in cancer surveillance. Int J Biol Markers. 25 (4), 236-242 (2010).
  53. Li, L., et al. NuSAP is degraded by APC/C-Cdh1 and its overexpression results in mitotic arrest dependent of its microtubules' affinity. Cell Signal. 19 (10), 2046-2055 (2007).
  54. Fabbro, M., et al. Cdk1/Erk2- and Plk1-dependent phosphorylation of a centrosome protein, cep55, is required for its recruitment to midbody and cytokinesis. Dev Cell. 9 (4), 477-488 (2005).
  55. Jeffery, J., Sinha, D., Srihari, S., Kalimutho, M., Khanna, K. K. Beyond cytokinesis: The emerging roles of CEP55 in tumorigenesis. Oncogene. 35 (6), 683-690 (2016).
  56. Feng, Z., et al. Overexpression of abnormal spindle-like microcephaly-associated (ASPM) increases tumor aggressiveness and predicts poor outcome in patients with lung adenocarcinoma. Transl Cancer Res. 10 (2), 983-997 (2021).
  57. Zheng, H., et al. Comprehensive pan-cancer analysis reveals NuSAP1 is a novel predictive biomarker for prognosis and immunotherapy response. Int J Biol Sci. 19 (14), 4689-4708 (2023).
  58. Verstappen, G. M., Pringle, S., Bootsma, H., Kroese, F. G. M. Epithelial-immune cell interplay in primary sjogren syndrome salivary gland pathogenesis. Nat Rev Rheumatol. 17 (6), 333-348 (2021).
  59. El Rayes, T., et al. Lung inflammation promotes metastasis through neutrophil protease-mediated degradation of Tsp-1. Proc Natl Acad Sci U S A. 112 (52), 16000-16005 (2015).
  60. Zhao, L., et al. PM2.5 and serum metabolome and insulin resistance, potential mediation by the gut microbiome: A population-based panel study of older adults in china. Environ Health Perspect. 130 (2), 27007(2022).
  61. Li, J., et al. PM2.5 exposure perturbs lung microbiome and its metabolic profile in mice. Sci Total Environ. 721, 137432(2020).
  62. Liao, J. H., et al. Network pharmacology-based strategy to investigate the mechanisms of artemisinin in treating primary sjogren's syndrome. BMC Immunol. 25 (1), 16(2024).
  63. Hu, Q., et al. JAK/STAT pathway: Extracellular signals, diseases, immunity, and therapeutic regimens. Front Bioeng Biotechnol. 11, 1110765(2023).
  64. Xue, C., et al. Evolving cognition of the JAK-STAT signaling pathway: Autoimmune disorders and cancer. Signal Transduct Target Ther. 8 (1), 204(2023).

Reprints and Permissions

Tags

Differentially Expressed GenesKEGG EnrichmentGO EnrichmentProtein Protein InteractionJAK2 STAT3 PathwayInflammatory CytokinesAnimal Model
Primary Sjogren's Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification
Contact Us
Recommend to Library

Privacy

Terms of Use

Policies

Copyright © 2026 MyJoVE Corporation. All rights reserved.