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

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 tumors^8,9,10. 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 species^12,13,14. 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. Place the mouse into the small animal anesthesia machine, activate the device, and use anesthesia with 5% isoflurane for 2 min.
   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 29^th 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 10%-30% 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 name	Sequence (5′ to 3′)
Mouse CCNA2 forward	CCCAGAAGTAGCAGAGTTTGTG
Mouse CCNA2 reverse	TTGTCCCGTGACTGTGTAGAG
Mouse ASPM forward	CTTATTCAGGCTATGTGGAGGA
Mouse ASPM reverse	CCAGGCTTGAATCTTGCAG
Mouse CCNB2 forward	TTGAAATTTGAGTTGGGTCGAC
Mouse CCNB2 reverse	CTGTTCAACATCAACCTCCC
Mouse NUSAP1 forward	CTCCCTCAAGTACAGTGACC
Mouse NUSAP1 reverse	TTTAACAACTTGGTTGCCCTC
Mouse CEP55 forward	CCGCCAGAATATGCAGCATCAAC
Mouse CEP55 reverse	AGTGGGAATGGCTGCTCTGTGA

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

6. 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.
7. 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.
8. 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...

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

Materials

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