Uncovering Immune Response Landscapes and Novel Biomarkers in Latent Endometrial Tuberculosis: Insights from RNA-Seq Transcriptome Profiling

Uncovering Immune Response Landscapes and Novel Biomarkers in Latent Endometrial Tuberculosis: Insights from RNA-Seq Transcriptome Profiling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Uncovering Immune Response Landscapes and Novel Biomarkers in Latent Endometrial Tuberculosis: Insights from RNA-Seq Transcriptome Profiling Bai Dai, Jing-ying Liu, De-Bang Li, Zhi-min Wang, Xiu-juan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5254793/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Latent Endometrial Tuberculosis (LETB) is a significant yet under-recognized cause of female infertility, particularly in TB-prevalent regions. Current diagnostic methods for LETB lack specificity, complicating early detection. Through RNA-Seq transcriptome profiling, we aimed to uncover distinct immune response landscapes and identify novel inflammation-related diagnostic markers for LETB. Our study included clinical diagnostics, histological examinations, and transcriptomic analyses comparing differentially expressed genes (DEGs) among control, LETB, and active TB groups. We identified seven candidate genes (IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A) uniquely associated with LETB. Bioinformatic analyses revealed these genes' significant roles in immune regulation, including leukocyte activation, cytokine signaling, and myeloid leukocyte-mediated immunity. Gene Set Enrichment Analysis (GSEA) confirmed their involvement in key immune pathways such as cytokine-cytokine receptor interaction and leukocyte transendothelial migration. Validation through qPCR and immunohistochemistry confirmed the differential expression of these biomarkers in LETB tissues. These findings provide new insights into LETB pathogenesis, suggesting potential biomarkers for enhanced early diagnosis and treatment, ultimately aiming to improve reproductive health outcomes for affected women. Latent Endometrial Tuberculosis Inflammatory Biomarkers RNA-Seq Immune Response Diagnosis Female Infertility Transcriptomic Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Latent tuberculosis (TB) is a global health concern, with an estimated 1.7 billion people worldwide harboring the infection, which has the potential to progress into active TB 1 . Among these, latent genital tuberculosis (GTB) significantly affects female reproductive health, often leading to infertility. One manifestation of GTB is latent endometrial tuberculosis (LETB), which can cause substantial damage to the endometrium, compromising fertility and overall reproductive health 2 . Understanding the specific immune responses and identifying reliable diagnostic markers for LETB are crucial for early diagnosis and treatment. Latent TB can reside in the body without causing immediate symptoms, making it challenging to diagnose and treat. In women, latent GTB, including LETB, can lead to severe reproductive issues such as infertility and menstrual irregularities. Studies indicate that latent GTB is responsible for 5–10% of infertility cases in regions with high TB prevalence 3 . The damage caused by the tuberculosis bacilli to the endometrial tissue impairs the normal function and structure of the uterus, which is essential for successful implantation and pregnancy. Highlighting the reproductive consequences of latent TB underscores the importance of early detection and intervention 4 . Diagnosing latent TB, especially LETB, poses significant challenges due to the lack of specific and sensitive diagnostic tools. Traditional diagnostic methods, including microbiological cultures and histopathological examinations, often fail to detect latent infections reliably 5 . These limitations necessitate the search for novel diagnostic targets that can accurately identify LETB at an early stage. Recent advances in molecular techniques, such as RNA-Seq, offer promising avenues for discovering unique diagnostic markers that reflect the underlying immune response associated with LETB 6 . The immune system plays a pivotal role in the pathogenesis of TB, including LETB. Inflammation is a hallmark of TB infection, driven by the host's immune response to Mycobacterium tuberculosis. Studies have shown that specific cytokine profiles, such as increased levels of IFN-γ and TNF-α, are associated with latent TB infections 7 . Understanding the specific immune landscapes associated with LETB can provide insights into the disease mechanism and identify inflammation-related markers crucial for diagnosis 8 . The interplay between TB bacilli and the host's immune system often results in a distinct immune profile, which can be exploited to develop specific diagnostic tools 9 . This study aims to utilize RNA-Seq transcriptome profiling to reveal the distinct immune response landscapes in LETB and identify inflammation-related diagnostic markers. By analyzing the gene expression patterns in endometrial tissues affected by latent TB, we aim to uncover specific biomarkers that can be used for early and accurate diagnosis. The study involves a comprehensive approach, including the selection of appropriate samples, RNA sequencing, and bioinformatics analysis to pinpoint potential diagnostic targets. The ultimate goal is to enhance the diagnostic accuracy for LETB, thereby improving early detection and treatment strategies. The findings from this study could significantly contribute to the understanding of LETB pathogenesis and offer new avenues for therapeutic intervention, ultimately improving reproductive health outcomes for affected women. 2. Materials and Methods 2.1. Ethics statement This study was approved by the ethical committee of the Affiliated Hospital of Inner Mongolia Medical University. Written informed consent was obtained from all study participants. All experimental procedures described in this study were carried out in accordance with the protocols approved by the ethical committee of the Affiliated Hospital of Inner Mongolia Medical University. All experimental designs, data collection, and analysis processes were performed in accordance with the standards of science and ethics. 2.2. Research sample 2.2.1 Retrospective analysis sample Medical records of 425 infertile patients who underwent the first cycle of IVF fresh embryo transfer at the Reproductive Medicine Center of Inner Mongolia Medical University Affiliated Hospital from April 2017 to April 2019 were collected. Inclusion criteria were as follows: (1) age between 22 and 40 years old; (2) no clinical manifestations of active tuberculosis; (3) exclusion of active tuberculosis by imaging and endometrial histopathology biopsy examination; (4) transfer of high-quality embryos of grade I-II. Exclusion criteria were: (1) patients with endometrial lesions such as endometrial polyps, complex endometrial hyperplasia, atypical endometrial hyperplasia, endometrial cancer, etc.; (2) pelvic endometriosis, uterine adenomyosis; (3) ovarian insufficiency, AMH < 1.1ng/ml; number of eggs obtained from bilateral ovaries ≤ 3; (4) patients solely undergoing assisted reproduction due to male factors. Specific grouping is shown in Table 1 . Table 1 Retrospective analysis sample grouping CG (TB-IGRA-, n = 278) LETB (TB-IGRA+, n = 147) NIG IG NIG IG 161 117 84 63 Note: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG, CD38-& CD138-); Non-Inflammatory (NIG, CD38+& CD138+) group; The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation. 2.2.2 Transcriptome analysis sample From April 2017 to April 2019, women aged 20 to 40 who underwent IVF treatment at the Reproductive Medicine Center of Inner Mongolia Medical University Affiliated Hospital due to tubal factors were selected. Prior to entering the IVF cycle, routine TB-IGRA testing was conducted. After obtaining informed consent for endometrial biopsy to exclude endometrial inflammatory lesions, endometrial biopsies were taken in the mid-luteal phase for pathological testing including CD38 and CD138. Additionally, a small amount of endometrial tissue was preserved in EP tubes and frozen at -80°C. Specific grouping is shown in Table 2 . Table 2 Transcriptome analysis sample grouping CG (TB-IGRA-, n = 6) LETB (TB-IGRA+, n = 6) TB (TB-IGRA+, n = 3) NIG IG NIG IG 3 3 3 3 3 Note: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG); Non-Inflammatory (NIG) group; Tuberculosis group (TB); The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation. 2.2.3 Validation analysis sample From April 2017 to April 2019, women aged 20 to 40 who underwent IVF treatment at the Reproductive Medicine Center of Inner Mongolia Medical University Affiliated Hospital due to tubal factors were selected. Prior to entering the IVF cycle, routine TB-IGRA testing was conducted. After obtaining informed consent for endometrial biopsy to exclude endometrial inflammatory lesions, endometrial biopsies were taken in the mid-luteal phase for pathological testing including CD38 and CD138. Additionally, a small amount of endometrial tissue was preserved in EP tubes and frozen at -80°C. Specific grouping is shown in Table 3 . Table 3 Validation analysis sample grouping CG (TB-IGRA-, n = 4) LETB (TB-IGRA+, n = 4) TB (TB-IGRA+, n = 4) NIG IG NIG IG 2 2 2 2 2 Note: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG); Non-Inflammatory (NIG) group; Tuberculosis group (TB); The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation. 2.3. ELISA for Tuberculosis Diagnosis In this study, we employed the TB-IGRA kit for adjunctive tuberculosis diagnosis using an ex vivo ELISA method. After collecting 4 mL of heparinized whole blood from each participant, aliquots were distributed into negative control, positive control, and patient culture mediums. Incubation at 37°C for 22 ± 2 hours followed, with subsequent plasma extraction by centrifugation at 4000 rpm for 10 minutes. Plasma samples were then analyzed for IFN-γ levels per the kit's instructions, adhering strictly to specified storage and operational conditions. This rigorous approach yielded critical data for tuberculosis diagnosis. 2.3. HE Staining and Immunohistochemistry Tissue specimens were fixed in 10% neutral buffered formalin, routinely dehydrated, cleared, embedded in paraffin, and sectioned at a thickness of 3µm. Hematoxylin and eosin (HE) staining was performed for histopathological evaluation. Immunohistochemistry was conducted using the EnVision two-step method. Primary antibodies against CD38, CD138, SPL1, and IL1B were purchased from Abcam. 2.4. Doppler Ultrasound Examination The Mindray color Doppler ultrasound diagnostic instruments are utilized, with probe frequencies ranging from 3.5 to 9.0 MHz. Through abdominal or vaginal examination, the uterus and bilateral adnexa are explored. Following the routine examination procedure, multi-directional, multi-sectional, and multi-angular scanning is performed. The location and size of lesions are recorded, and evaluation includes internal and posterior echogenicity, morphology, boundaries, and relationships with surrounding structures. Lesions are subjected to color Doppler detection, and images are stored. 2.5. RNA extraction Total RNA was extracted from the tissue using TRIzol® Reagent following the manufacturer’s instructions. Subsequently, RNA quality was assessed using the 5300 Bioanalyzer (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). Only high-quality RNA samples (OD260/280 = 1.8 ~ 2.2, OD260/230 ≥ 2.0, RIN ≥ 6.5, 28S:18S ≥ 1.0, > 1µg) were utilized for constructing the sequencing library. 2.6. Library preparation and Sequencing RNA purification, reverse transcription, library construction, and sequencing were conducted at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China) following the manufacturer’s instructions (Illumina, San Diego, CA). The uterine endometrial tissue RNA-seq transcriptome library was prepared using Illumina® Stranded mRNA Prep, Ligation from Illumina (San Diego, CA) with 1µg of total RNA. Initially, messenger RNA was isolated via the polyA selection method using oligo(dT) beads and subsequently fragmented using a fragmentation buffer. Next, double-stranded cDNA was synthesized employing the SuperScript double-stranded cDNA synthesis kit (Invitrogen, CA) with random hexamer primers (Illumina). The synthesized cDNA underwent end-repair, phosphorylation, and 'A' base addition according to Illumina's library construction protocol. Libraries were size-selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose, followed by PCR amplification using Phusion DNA polymerase (NEB) for 15 PCR cycles. After quantification using Qubit 4.0, the paired-end RNA-seq sequencing library was sequenced using the NovaSeq 6000 sequencer (2 × 150 bp read length). The raw paired end reads were trimmed and quality controlled by fastp 10 with default parameters. Then clean reads were separately aligned to reference genome with orientation mode using HISAT 11 software. The mapped reads of each sample were assembled by StringTie 12 in a reference-based approach (Table 4 ). Table 4 The mRNA sequencing data statistics Sample Raw reads Raw bases Clean reads Clean bases Error rate (%) Q20 (%) Q30 (%) GC (%) CG NIG 47350426 7149914326 46067584 6758894916 0.0268 97.20 92.57 51.94 54298556 8199081956 52713700 7623789224 0.0264 97.37 92.95 51.93 56356580 8509843580 54883286 7950335261 0.0266 97.28 92.75 52.33 IG 43141658 6514390358 41434754 6078093406 0.0267 97.24 92.74 51.37 53446106 8070362006 51813696 7511173014 0.0263 97.40 93.03 52.27 53016154 8005439254 51276054 7392516867 0.0264 97.37 92.97 52.10 LETB NIG 48829076 7373190476 47420486 6915852500 0.0263 97.42 93.05 52.33 51965434 7846780534 49786886 7289164008 0.0272 97.01 92.22 50.79 46722478 7055094178 45312204 6627150568 0.0262 97.48 93.15 52.94 IG 48005458 7248824158 46989584 6893496419 0.0269 97.19 92.49 51.79 55793572 8424829372 53772540 7800850282 0.0266 97.27 92.73 52.11 49896196 7534325596 48599792 7113620448 0.0266 97.28 92.73 51.74 TB 50342790 7601761290 48473942 7093253613 0.0265 97.31 92.80 50.46 46850508 7074426708 45472326 6663625083 0.0272 97.07 92.27 51.94 55172404 8331033004 53863158 7920817759 0.0265 97.34 92.85 51.88 Note: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG); Non-Inflammatory (NIG) group; Tuberculosis group (TB); The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation. 2.7. Differential expression analysis and Functional enrichment To identify differentially expressed genes (DEGs) between two distinct samples, transcript expression levels were quantified using the transcripts per million reads (TPM) method. Gene abundances were quantified using RSEM 13 Differential expression analysis was conducted using either DESeq2 or DEGseq. DEGs meeting the criteria of |log2FC| ≥ 1 and FDR ≤ 0.05 (DESeq2) 14 or FDR ≤ 0.001 (DEGseq) 15 were considered significantly differentially expressed. Additionally, functional enrichment analysis, encompassing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, was performed to identify DEGs significantly enriched in GO terms and metabolic pathways, with a Bonferroni-corrected P-value ≤ 0.05 compared to the whole-transcriptome background. GO functional enrichment and KEGG pathway analysis were conducted using Goatools and KOBAS 16 , respectively. 2.8. Set Enrichment Analysis (GSEA) In this study, we conducted single-gene gene set enrichment analysis (GSEA) utilizing R programming language and its packages, specifically 'limma' for differential gene expression analysis, 'ggplot2' and 'pheatmap' for data visualization, 'clusterProfiler' and 'enrichplot' for enrichment analysis against databases such as KEGG, 'org.Hs.eg.db' for gene identifier conversion, and 'patchwork' along with 'gseaplot2' for the visualization of the enrichment results. This approach allowed us to delve into the enrichment of the gene sets associated with the gene of interest within immune-related biological pathways. 2.9. Quantitative Real-Time PCR (qPCR) Analysis In this study, we employed quantitative real-time polymerase chain reaction (qPCR) technology to perform relative quantification analysis of gene expression in tissue samples. The experimental workflow included RNA extraction (using the OmniPlant RNA Kit from Beijing Kangwei Century Biotechnology Co., Ltd.), reverse transcription (with the HiScript Q RT SuperMix for qPCR from Nanjing Novozymes Biotechnology Co., Ltd.), and relative quantification of target genes versus reference genes (β-actin and GAPDH) using the ChamQ SYBR Color qPCR Master Mix (also from Nanjing Novozymes Biotechnology Co., Ltd.). Amplification and data acquisition were conducted using the LineGene9600plus real-time quantitative PCR instrument (Hangzhou Bioer Technology Co., Ltd.). Primers were synthesized with PAGE purification by Shenggong Bioengineering Co., Ltd., ensuring specificity and efficiency (Table 5 ). Table 5 List of qPCR Primers Required for Gene Validation Primer Base Sequence (5' to 3') ssIFI30-F TGTGACCCTCTACTATGAAGCA ssIFI31-R GGCACTTGAACTCCCACC ssHCK-F GGAGGCAATACATTCTCAAA ssHCK-R ATACAGGGCAACCACGAT ssSP1-F CACTGGAGGTGTCTGACGG ssSP1-R TGCTTGGACGAGAACTGGAA ssIL1B-F CTCGCCAGTGAAATGAT ssIL1B-R AAGCCCTTGCTGTAGTG ssITGB2-F CCCTCACCCTGTGGCAAGT ssITGB2-R TGCTCCAGCGTGTAGGC ssFCGR2A-F CTCCATCCCACAAGCAA ssFCGR2A-R CAGTCGCAATGACCACA 2.10. Statistical analysis Statistical significance was determined by one-way ANOVA using SPSS software (SPSS 21.0 for Windows). All productionrelated data are reported as the mean ± SE, and differences were considered significant at p < 0.05. 3. Results 3.1. Absence of reliable diagnostic markers for Latent Endometrial Tuberculosis (LETB) The endometrium often accompanies inflammation during the immune response against latent tuberculosis. Our study aimed to explore reliable inflammatory biomarkers for LETB diagnosis. Initially, we investigated pregnancy outcomes by comparing the Control Group (CG) and LETB, using the following criteria for grouping. Current diagnostic standards utilized positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA) results as indicative of LETB, along with positivity for CD38 and CD138, suggesting inflammation. The results revealed no significant differences in pregnancy outcomes between the Inflammatory (IG) and Non-Inflammatory (NIG) groups (P > 0.05), indicating the lack of specific diagnostic markers for LETB (Fig. 1 A). Subsequently, we compared Hematoxylin and Eosin (HE) staining results between the control group and LETB, showing no significant differences. Immunohistochemistry (IHC) using CD38 and CD138 as inflammatory markers also showed no significant difference in inflammation levels between LETB and the control group (Fig. 1 B). These findings further support the conclusion that LETB lacks specific inflammatory markers. Additionally, comparison of intrauterine ultrasound images between the control group and LETB showed no significant differences in endometrial morphology, thickness, or uterine cavity volume. Overall, our experimental results indicate the absence of specific inflammatory markers associated with LETB (Fig. 1 C). 3.2. Transcriptomic Analysis Reveals Novel Inflammatory Biomarkers for LETB Transcriptomic analysis aimed to uncover novel inflammatory biomarkers associated with latent endometrial tuberculosis (LETB). Through a systematic screening process, a series of pairwise intersections were performed among the control group (CG), LETB, and tuberculosis group (TB), followed by a subsequent intersection of the three sets. This approach yielded intersection A, representing a common pool of differentially expressed genes shared across the groups, thus serving as potential diagnostic targets. Further refinement involved excluding interference from common inflammatory genes. By intersecting CG-NIG and CG-IG, intersection B, comprising common inflammatory genes, was identified. Subsequently, genes in intersection B were removed from gene set A to derive gene set C, which specifically captured LETB-related inflammatory biomarkers. Lastly, intersections between LETB-NIG and LETB-IG revealed a specific set of inflammatory genes induced by latent tuberculosis, constituting the final candidate gene set E (Fig. 2 A). The differential expression patterns of these potential biomarkers were visualized in a heatmap, showcasing downregulated genes in orange and upregulated genes in green across CG, LETB, and TB groups. Seven candidate genes, identified through rigorous screening, were highlighted in red (Fig. 2 B). 3.3. Unveiling Distinct Immune Response Functional Enrichment and Protein Interaction Networks in LETB-Associated Candidate Genes In our study, we conducted an in-depth analysis of candidate genes potentially indicative of LETB using comprehensive bioinformatics approaches. The results from Gene Ontology (GO) and KEGG pathway analyses provided detailed insights into the biological processes, cellular components, and molecular functions associated with these genes. GO analysis highlighted their pivotal role in immune response regulation, particularly in processes such as positive regulation of myeloid leukocyte-mediated immunity, leukocyte degranulation, and cell activation. Additionally, the cellular localization of these genes, including tertiary granule membrane, secretory granule membrane, and membrane rafts, suggests their involvement in intracellular signaling and material transport. At the molecular function level, the encoded proteins exhibited diverse functionalities crucial for cellular signaling, such as complement binding, protein tyrosine kinase activity, and STAT family protein binding (Fig. 3 A). Furthermore, KEGG pathway analysis revealed associations between these genes and tuberculosis-related signaling pathways, shedding light on their roles in disease onset and progression (Fig. 3 B). Protein-protein interaction (PPI) analysis unveiled interactive relationships within cellular signaling networks. Notably, key interacting proteins identified, such as HCK, SPI1, IL1B, ITGB2, and FCGR2A, may play central roles in immune response and cellular signaling. These interactions may represent critical nodes in immune regulation processes. Integrating these findings, we identified candidate genes closely associated with LETB, including IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A (Fig. 3 C). 3.4. Validating Tuberculosis Genomic Markers: Bridging Transcriptomics with Clinical Application In our study, we conducted a comprehensive analysis using Gene Set Enrichment Analysis (GSEA) to investigate the potential roles of six key genes (IF130, HCK, SPI1, IL1B, ITGB2, and FCGR2A) identified in endometrial tissues with latent tuberculosis infection, within immune-related biological pathways. Our analysis revealed significant enrichment of these genes in multiple pathways closely associated with immune response, including “KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION," "KEGG_AXON_GUIDANCE,” “KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION,” and "KEGG_CHEMOKINE_SIGNALING_PATHWAY." These pathways share a common theme in their core roles in communication, migration, and signal transduction among immune cells. Particularly, the enrichment of IF130, HCK, SPI1, IL1B, ITGB2, and FCGR2A in the “KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION” pathway suggests their potential involvement in regulating the interaction between cytokines and their receptors, a crucial step in immune response. Furthermore, the enrichment of ITGB2 in the “KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION” pathway implies its importance in the migration of immune cells across the endothelial layer, crucial for immune cell trafficking to sites of infection or inflammation. The enrichment of FCGR2A in the "KEGG_CHEMOKINE_SIGNALING_PATHWAY" pathway may be related to the chemotaxis and signaling mechanisms of immune cells (Fig. 4 ). Based on the analysis, we validated potential LETB biomarkers through qPCR, HE staining, and immunohistochemistry. The qPCR results indicated that the relative expression of IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A was significantly higher in the inflammatory group (IG) compared to the non-inflammatory group (NIG) (p < 0.05). HE staining revealed notable inflammatory cell infiltration and structural alterations in the IG, whereas the NIG maintained relatively normal tissue architecture. Additionally, based on qPCR data, we selected HCK and ITGB2 for immunohistochemical validation, which demonstrated significantly enhanced expression in the IG. These findings collectively suggest that these biomarkers hold substantial clinical diagnostic potential in inflammatory conditions (Fig. 5 ). 4. Discussion Our study aimed to identify specific biomarkers for Latent Endometrial Tuberculosis (LETB) by employing RNA-Seq transcriptome profiling. Initial comparisons revealed that conventional diagnostic methods, including TB-IGRA and markers like CD38 and CD138, were insufficient in distinguishing LETB from control samples based on pregnancy outcomes, histological examinations, and immunohistochemistry (IHC) results. Subsequent transcriptomic analyses identified seven candidate genes (IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A) uniquely associated with LETB, which were further validated through quantitative PCR (qPCR) and IHC, confirming their differential expression in LETB tissues. The immune response in LETB is characterized by unique inflammatory markers that differentiate it from other forms of tuberculosis and from healthy control endometrial tissues. Our study found that genes such as IFI30 and SPI1 play significant roles in myeloid leukocyte-mediated immunity and leukocyte degranulation, indicating a robust immune response specific to LETB. Gene Ontology (GO) and KEGG pathway analyses revealed these genes' involvement in critical immune pathways, such as cytokine-cytokine receptor interaction and leukocyte transendothelial migration. These pathways are essential for immune cell communication, migration, and activation, which are crucial for mounting an effective response against latent infections. The six genes identified (IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A) have been implicated in various immune regulatory mechanisms and inflammation associated with tuberculosis. IFI30, known for its role in antigen processing and presentation, is critical in the immune system's ability to recognize and respond to Mycobacterium tuberculosis 17 , 18 . HCK and SPI1 are involved in the signaling pathways that activate macrophages and other immune cells, highlighting their importance in controlling TB infection 17 , 18 . IL1B, a key pro-inflammatory cytokine, has been shown to be elevated in TB infections, contributing to the inflammatory milieu necessary for fighting the pathogen 19 . ITGB2 and FCGR2A are integral to leukocyte adhesion and migration, facilitating the movement of immune cells to sites of infection 20 – 23 . Building on our findings, future research should focus on expanding sample sizes and integrating proteomic and metabolomic data to provide a more comprehensive understanding of LETB's impact on infertility. Longitudinal studies are needed to explore the progression of LETB and its direct effects on reproductive outcomes. Additionally, functional studies of the identified biomarkers will help elucidate their roles in LETB pathogenesis and pave the way for the development of targeted diagnostic tools and treatments, ultimately improving fertility outcomes for affected women. 5. Conclusion This study identifies and validates novel inflammatory biomarkers for LETB, offering new insights into its pathogenesis and potential avenues for improved diagnostic and therapeutic strategies. The identified biomarkers, including IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A, hold promise for enhancing the early detection and treatment of LETB, thereby improving reproductive health outcomes for affected women. Declarations Author Contributions: Xiu-Juan Chen conceived and supervised the study, Bai Dai and Zhi-min Wang designed and performed the experiments.Jing-ying Liu and Xin Chen analyzed the data and wrote the paper. Funding: This work was supported by the science and technology project of Inner Mongolia Autonomous Region, China (2021GG0389), Natural Science Foundation of Inner Mongolia Autonomous Region, China (2023QN08038), Inner Mongolia Medical University Youth Project (YKD2024QN004), Inner Mongolia Archives Management Science and Technology Project of Inner Mongolia Autonomous Region, China (2023-10), Inner Mongolia Medical University Laboratory Open Fund Project of the Inner Mongolia Autonomous Region, China (2023ZN04), research project of Inner Mongolia Medical University Affliated Hospital of the Inner Mongolia Autonomous Region, China (2023NYFYGGO14). Acknowledgments: We are grateful to the YiKang and BeiKang limited liability company for their support with the single cell sequencing experiment. Conflicts of Interest: The authors declare no conflicts of interest. Data availability: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Chakaya, J. et al. Global Tuberculosis Report 2020 - Reflections on the Global TB burden, treatment and prevention efforts. Int J Infect Dis 113 Suppl 1 , S7-S12 (2021). https://doi.org:10.1016/j.ijid.2021.02.107 Sharma, J. B., Sharma, E., Sharma, S. & Dharmendra, S. Female genital tuberculosis: Revisited. Indian J Med Res 148 , S71-S83 (2018). https://doi.org:10.4103/ijmr.IJMR_648_18 Ghosh, K., Ghosh, K. & Chowdhury, J. R. Tuberculosis and female reproductive health. J Postgrad Med 57 , 307-313 (2011). https://doi.org:10.4103/0022-3859.90082 Flynn, J. L. & Chan, J. Tuberculosis: latency and reactivation. Infect Immun 69 , 4195-4201 (2001). https://doi.org:10.1128/IAI.69.7.4195-4201.2001 Houben, R. M. & Dodd, P. J. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. PLoS Med 13 , e1002152 (2016). https://doi.org:10.1371/journal.pmed.1002152 MacLean, E. et al. A systematic review of biomarkers to detect active tuberculosis. Nat Microbiol 4 , 748-758 (2019). https://doi.org:10.1038/s41564-019-0380-2 Wallis, R. S., Peppard, T. & Hermann, D. Month 2 culture status and treatment duration as predictors of recurrence in pulmonary tuberculosis: model validation and update. PLoS One 10 , e0125403 (2015). https://doi.org:10.1371/journal.pone.0125403 Barry, C. E., 3rd et al. The spectrum of latent tuberculosis: rethinking the biology and intervention strategies. Nat Rev Microbiol 7 , 845-855 (2009). https://doi.org:10.1038/nrmicro2236 Goletti, D., Petruccioli, E., Joosten, S. A. & Ottenhoff, T. H. Tuberculosis Biomarkers: From Diagnosis to Protection. Infect Dis Rep 8 , 6568 (2016). https://doi.org:10.4081/idr.2016.6568 Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34 , i884-i890 (2018). https://doi.org:10.1093/bioinformatics/bty560 Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12 , 357-360 (2015). https://doi.org:10.1038/nmeth.3317 Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33 , 290-295 (2015). https://doi.org:10.1038/nbt.3122 Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12 , 323 (2011). https://doi.org:10.1186/1471-2105-12-323 Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15 , 550 (2014). https://doi.org:10.1186/s13059-014-0550-8 Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26 , 136-138 (2010). https://doi.org:10.1093/bioinformatics/btp612 Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res 39 , W316-322 (2011). https://doi.org:10.1093/nar/gkr483 Panda, S. et al. Identification of differentially recognized T cell epitopes in the spectrum of tuberculosis infection. Nat Commun 15 , 765 (2024). https://doi.org:10.1038/s41467-024-45058-9 Mai, D. et al. Exposure to Mycobacterium remodels alveolar macrophages and the early innate response to Mycobacterium tuberculosis infection. PLoS Pathog 20 , e1011871 (2024). https://doi.org:10.1371/journal.ppat.1011871 Yin, J. et al. Common variants of pro-inflammatory gene IL1B and interactions with PPP1R13L and POLR1G in relation to lung cancer among Northeast Chinese. Sci Rep 13 , 7352 (2023). https://doi.org:10.1038/s41598-023-34069-z Blazevic, A. et al. Phase 1 Open-Label Dose Escalation Trial for the Development of a Human Bacillus Calmette-Guerin Challenge Model for Assessment of Tuberculosis Immunity In Vivo. J Infect Dis 229 , 1498-1508 (2024). https://doi.org:10.1093/infdis/jiad441 Liu, Y. et al. DNA methylation of ITGB2 contributes to allopurinol hypersensitivity. Clin Immunol 248 , 109250 (2023). https://doi.org:10.1016/j.clim.2023.109250 Shi, X., Ma, Y., Li, H. & Yu, H. Association between FCGR2A rs1801274 and MUC5B rs35705950 variations and pneumonia susceptibility. BMC Med Genet 21 , 71 (2020). https://doi.org:10.1186/s12881-020-01005-1 Lu, S. et al. Fc fragment of immunoglobulin G receptor IIa (FCGR2A) as a new potential prognostic biomarker of esophageal squamous cell carcinoma. Chin Med J (Engl) 135 , 482-484 (2021). https://doi.org:10.1097/CM9.0000000000001776 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 09 Dec, 2024 Reviewers agreed at journal 08 Dec, 2024 Reviews received at journal 11 Nov, 2024 Reviewers agreed at journal 09 Nov, 2024 Reviewers agreed at journal 08 Nov, 2024 Reviewers invited by journal 06 Nov, 2024 Editor assigned by journal 06 Nov, 2024 Editor invited by journal 04 Nov, 2024 Submission checks completed at journal 01 Nov, 2024 First submitted to journal 13 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5254793","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376711806,"identity":"554a7c75-93bb-4267-953a-c28248c9c3b1","order_by":0,"name":"Bai Dai","email":"","orcid":"","institution":"Inner Mongolia Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bai","middleName":"","lastName":"Dai","suffix":""},{"id":376711807,"identity":"c4634bf9-b90c-447b-9886-c7aa61c971ca","order_by":1,"name":"Jing-ying Liu","email":"","orcid":"","institution":"Inner Mongolia Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing-ying","middleName":"","lastName":"Liu","suffix":""},{"id":376711808,"identity":"072316ab-4b6a-4201-9978-2e2726b113b3","order_by":2,"name":"De-Bang Li","email":"","orcid":"","institution":"Inner Mongolia Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"De-Bang","middleName":"","lastName":"Li","suffix":""},{"id":376711809,"identity":"7a8ca866-3a42-4b9f-a53e-fc4ec75e76a2","order_by":3,"name":"Zhi-min Wang","email":"","orcid":"","institution":"Inner Mongolia Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhi-min","middleName":"","lastName":"Wang","suffix":""},{"id":376711810,"identity":"5ce175ea-bc3a-4b0b-abd2-1bfe4c6b4564","order_by":4,"name":"Xiu-juan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACfmbGxgcfKmzq+xkYG4jTItnefNhwxpk0xpkNxGoxOHMsTZq35TDjhgPEOszgRo6Z5MyGNGbj84fbHvxgsJPTJWSZ5I0cY4uPO2zYzG4kthv2MCQbmxGyju9GjuHNmWfSeMxuMLZJ8DAcSNxG0IU3cgykedsOSxj3H2yT/EOMFoEzx5JAWgwMGBLbpImyBRbICRI3gFpkDIjwCywqE/j7jz+TfFNhJ0dQCxowIE35KBgFo2AUjAIcAAAU+kkLZoak4gAAAABJRU5ErkJggg==","orcid":"","institution":"Inner Mongolia Medical College Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiu-juan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-10-13 10:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5254793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5254793/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-89483-2","type":"published","date":"2025-04-10T16:05:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68942011,"identity":"064307b1-7850-4e39-aa38-2aec0fb43bfd","added_by":"auto","created_at":"2024-11-13 18:13:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196669,"visible":true,"origin":"","legend":"\u003cp\u003eLack of Reliable Diagnostic Markers for Latent Endometrial Tuberculosis (LETB).(A) Comparison of Pregnancy Outcomes between Control Group (CG) and LETB. The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation. The study reveals no significant differences in pregnancy outcomes between the Inflammatory (IG) and Non-Inflammatory (NIG) groups (P \u0026gt; 0.05). (\"/\" symbol indicates unavailable data). (B) Comparison of Hematoxylin and Eosin (HE) staining and Immunohistochemistry (IHC) results between CG and LETB. CD38 and CD138 serve as marker genes. Scale bar, 25 mm. (C) Comparison of intrauterine ultrasound images between CG and LETB. The morphology of the endometrial cavity shows uniformity without thinning of the endometrium (typically defined as \u0026lt;7mm). No significant differences were observed between CG and LETB in terms of endometrial morphology, thickness, and uterine cavity volume.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5254793/v1/82f539daaf5f9b18ca601406.png"},{"id":68942010,"identity":"18cb5d66-03b2-4028-9112-5516c4b81794","added_by":"auto","created_at":"2024-11-13 18:13:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1084923,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic Analysis Attempts to Identify Novel Inflammatory Biomarkers for LETB. (A) The Venn diagram illustrates the screening process of potential inflammatory biomarkers. Initially, intersections were taken between the control group (CG), latent endometrial tuberculosis group (LETB), and tuberculosis group (TB) pairwise, followed by a subsequent intersection of the three sets to obtain intersection A, representing the common differentially expressed gene set A, serving as the candidate genes for potential diagnostic targets. Subsequently, interference from common inflammatory genes was excluded from the candidate gene set A, resulting in gene set C. Specifically, intersections were taken between CG-NIG and CG-IG to obtain the intersection B of common inflammatory genes, followed by the removal of genes contained in B from gene set A to derive gene set C. Finally, a specific set of inflammatory genes induced by latent tuberculosis was identified from gene set C as the final candidate gene set E. The method involved taking intersections between LETB-NIG and LETB-IG to obtain intersection D, followed by taking the intersection of gene sets C and D to derive gene set E. (B) Heatmap displaying the differential expression of potential inflammatory biomarkers across the control group (CG), latent endometrial tuberculosis group (LETB), and tuberculosis group (TB). Downregulated genes are depicted in orange, while upregulated genes are depicted in green. Seven candidate genes, identified through further screening, are highlighted in red.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5254793/v1/c1d2f28f1ac560c2bb92338e.png"},{"id":68942015,"identity":"87d10c79-76ec-4e59-b2fc-e6bcecf783b7","added_by":"auto","created_at":"2024-11-13 18:13:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1034782,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Enrichment Analysis of Candidate Genes and Protein-Protein Interaction Network Exploration. (A) Gene Ontology (GO) analysis highlights the top ten terms in biological processes (BP), cellular components (CC), and molecular functions (MF) for a set of candidate genes. Z-scores and log fold changes (logFC) quantify expression trends, with 'upregulated' indicating increased expression and 'decreasing'/'increasing' reflecting changes in expression levels. (B) The overall KEGG pathway classifcation of the genes. Top 10 up-regulated KEGG pathways are presented. (C) For the candidate genes potentially indicative of LETB, protein-protein interaction (PPI) analysis was conducted using STRING (PPI enrichment p-value \u0026lt; 1.0e-16). Line thickness indicates the confidence level of interactions, with only high-confidence interactions displayed. The PPI network is enriched for immune response pathway genes (false discovery rate = 5.51e-06).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5254793/v1/916ed9d4bcda5e292aa0d0d4.png"},{"id":68942014,"identity":"f29c7982-f540-487d-98ad-c4614e2c5428","added_by":"auto","created_at":"2024-11-13 18:13:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":829171,"visible":true,"origin":"","legend":"\u003cp\u003eGene set enrichment analysis of potential LETB biomarkers. Analyses of IFI30 (A) HCK (B) SPI1 (C) IL1B (D) ITGB2 (E) FCGR2A (F) were carried out. LETB, latent endometrial tuberculosis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5254793/v1/7d87305df75815e93caea195.png"},{"id":68942422,"identity":"865341be-b582-4229-b6c1-17e03ee9aedc","added_by":"auto","created_at":"2024-11-13 18:21:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":346572,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of Potential LETB Biomarkers through Clinical Samples. (A) Validation of potential LETB biomarkers by qPCR; * indicates that the relative expression differs (p \u0026lt; 0.05). (B) Comparison of HE staining results between the Inflammatory (IG) and Non-Inflammatory (NIG) groups. (C) Validation of potential LETB biomarkersby Immunohistochemistry.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5254793/v1/1e1ea7f415f91bdb926a52fc.png"},{"id":80558940,"identity":"7a085920-987c-4659-a9e1-df0fc08aadda","added_by":"auto","created_at":"2025-04-14 16:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4593651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5254793/v1/8754663f-3f08-431b-a575-7ea1668cf6cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering Immune Response Landscapes and Novel Biomarkers in Latent Endometrial Tuberculosis: Insights from RNA-Seq Transcriptome Profiling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLatent tuberculosis (TB) is a global health concern, with an estimated 1.7\u0026nbsp;billion people worldwide harboring the infection, which has the potential to progress into active TB\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among these, latent genital tuberculosis (GTB) significantly affects female reproductive health, often leading to infertility. One manifestation of GTB is latent endometrial tuberculosis (LETB), which can cause substantial damage to the endometrium, compromising fertility and overall reproductive health\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Understanding the specific immune responses and identifying reliable diagnostic markers for LETB are crucial for early diagnosis and treatment.\u003c/p\u003e \u003cp\u003eLatent TB can reside in the body without causing immediate symptoms, making it challenging to diagnose and treat. In women, latent GTB, including LETB, can lead to severe reproductive issues such as infertility and menstrual irregularities. Studies indicate that latent GTB is responsible for 5\u0026ndash;10% of infertility cases in regions with high TB prevalence\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The damage caused by the tuberculosis bacilli to the endometrial tissue impairs the normal function and structure of the uterus, which is essential for successful implantation and pregnancy. Highlighting the reproductive consequences of latent TB underscores the importance of early detection and intervention\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDiagnosing latent TB, especially LETB, poses significant challenges due to the lack of specific and sensitive diagnostic tools. Traditional diagnostic methods, including microbiological cultures and histopathological examinations, often fail to detect latent infections reliably\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These limitations necessitate the search for novel diagnostic targets that can accurately identify LETB at an early stage. Recent advances in molecular techniques, such as RNA-Seq, offer promising avenues for discovering unique diagnostic markers that reflect the underlying immune response associated with LETB\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe immune system plays a pivotal role in the pathogenesis of TB, including LETB. Inflammation is a hallmark of TB infection, driven by the host's immune response to Mycobacterium tuberculosis. Studies have shown that specific cytokine profiles, such as increased levels of IFN-γ and TNF-α, are associated with latent TB infections\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Understanding the specific immune landscapes associated with LETB can provide insights into the disease mechanism and identify inflammation-related markers crucial for diagnosis\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The interplay between TB bacilli and the host's immune system often results in a distinct immune profile, which can be exploited to develop specific diagnostic tools\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aims to utilize RNA-Seq transcriptome profiling to reveal the distinct immune response landscapes in LETB and identify inflammation-related diagnostic markers. By analyzing the gene expression patterns in endometrial tissues affected by latent TB, we aim to uncover specific biomarkers that can be used for early and accurate diagnosis. The study involves a comprehensive approach, including the selection of appropriate samples, RNA sequencing, and bioinformatics analysis to pinpoint potential diagnostic targets.\u003c/p\u003e \u003cp\u003eThe ultimate goal is to enhance the diagnostic accuracy for LETB, thereby improving early detection and treatment strategies. The findings from this study could significantly contribute to the understanding of LETB pathogenesis and offer new avenues for therapeutic intervention, ultimately improving reproductive health outcomes for affected women.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Ethics statement\u003c/h2\u003e \u003cp\u003e This study was approved by the ethical committee of the Affiliated Hospital of Inner Mongolia Medical University. Written informed consent was obtained from all study participants. All experimental procedures described in this study were carried out in accordance with the protocols approved by the ethical committee of the Affiliated Hospital of Inner Mongolia Medical University. All experimental designs, data collection, and analysis processes were performed in accordance with the standards of science and ethics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Research sample\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Retrospective analysis sample\u003c/h2\u003e \u003cp\u003eMedical records of 425 infertile patients who underwent the first cycle of IVF fresh embryo transfer at the Reproductive Medicine Center of Inner Mongolia Medical University Affiliated Hospital from April 2017 to April 2019 were collected. Inclusion criteria were as follows: (1) age between 22 and 40 years old; (2) no clinical manifestations of active tuberculosis; (3) exclusion of active tuberculosis by imaging and endometrial histopathology biopsy examination; (4) transfer of high-quality embryos of grade I-II. Exclusion criteria were: (1) patients with endometrial lesions such as endometrial polyps, complex endometrial hyperplasia, atypical endometrial hyperplasia, endometrial cancer, etc.; (2) pelvic endometriosis, uterine adenomyosis; (3) ovarian insufficiency, AMH\u0026thinsp;\u0026lt;\u0026thinsp;1.1ng/ml; number of eggs obtained from bilateral ovaries\u0026thinsp;\u0026le;\u0026thinsp;3; (4) patients solely undergoing assisted reproduction due to male factors. Specific grouping is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRetrospective analysis sample grouping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCG (TB-IGRA-, n\u0026thinsp;=\u0026thinsp;278)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLETB (TB-IGRA+, n\u0026thinsp;=\u0026thinsp;147)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG, CD38-\u0026amp; CD138-); Non-Inflammatory (NIG, CD38+\u0026amp; CD138+) group; The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Transcriptome analysis sample\u003c/h2\u003e \u003cp\u003eFrom April 2017 to April 2019, women aged 20 to 40 who underwent IVF treatment at the Reproductive Medicine Center of Inner Mongolia Medical University Affiliated Hospital due to tubal factors were selected. Prior to entering the IVF cycle, routine TB-IGRA testing was conducted. After obtaining informed consent for endometrial biopsy to exclude endometrial inflammatory lesions, endometrial biopsies were taken in the mid-luteal phase for pathological testing including CD38 and CD138. Additionally, a small amount of endometrial tissue was preserved in EP tubes and frozen at -80\u0026deg;C. Specific grouping is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTranscriptome analysis sample grouping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCG (TB-IGRA-, n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLETB (TB-IGRA+, n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTB (TB-IGRA+, n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG); Non-Inflammatory (NIG) group; Tuberculosis group (TB); The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Validation analysis sample\u003c/h2\u003e \u003cp\u003eFrom April 2017 to April 2019, women aged 20 to 40 who underwent IVF treatment at the Reproductive Medicine Center of Inner Mongolia Medical University Affiliated Hospital due to tubal factors were selected. Prior to entering the IVF cycle, routine TB-IGRA testing was conducted. After obtaining informed consent for endometrial biopsy to exclude endometrial inflammatory lesions, endometrial biopsies were taken in the mid-luteal phase for pathological testing including CD38 and CD138. Additionally, a small amount of endometrial tissue was preserved in EP tubes and frozen at -80\u0026deg;C. Specific grouping is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation analysis sample grouping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCG (TB-IGRA-, n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLETB (TB-IGRA+, n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTB (TB-IGRA+, n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG); Non-Inflammatory (NIG) group; Tuberculosis group (TB); The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. ELISA for Tuberculosis Diagnosis\u003c/h2\u003e \u003cp\u003eIn this study, we employed the TB-IGRA kit for adjunctive tuberculosis diagnosis using an ex vivo ELISA method. After collecting 4 mL of heparinized whole blood from each participant, aliquots were distributed into negative control, positive control, and patient culture mediums. Incubation at 37\u0026deg;C for 22\u0026thinsp;\u0026plusmn;\u0026thinsp;2 hours followed, with subsequent plasma extraction by centrifugation at 4000 rpm for 10 minutes. Plasma samples were then analyzed for IFN-γ levels per the kit's instructions, adhering strictly to specified storage and operational conditions. This rigorous approach yielded critical data for tuberculosis diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3. HE Staining and Immunohistochemistry\u003c/h2\u003e \u003cp\u003eTissue specimens were fixed in 10% neutral buffered formalin, routinely dehydrated, cleared, embedded in paraffin, and sectioned at a thickness of 3\u0026micro;m. Hematoxylin and eosin (HE) staining was performed for histopathological evaluation. Immunohistochemistry was conducted using the EnVision two-step method. Primary antibodies against CD38, CD138, SPL1, and IL1B were purchased from Abcam.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Doppler Ultrasound Examination\u003c/h2\u003e \u003cp\u003eThe Mindray color Doppler ultrasound diagnostic instruments are utilized, with probe frequencies ranging from 3.5 to 9.0 MHz. Through abdominal or vaginal examination, the uterus and bilateral adnexa are explored. Following the routine examination procedure, multi-directional, multi-sectional, and multi-angular scanning is performed. The location and size of lesions are recorded, and evaluation includes internal and posterior echogenicity, morphology, boundaries, and relationships with surrounding structures. Lesions are subjected to color Doppler detection, and images are stored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. RNA extraction\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from the tissue using TRIzol\u0026reg; Reagent following the manufacturer\u0026rsquo;s instructions. Subsequently, RNA quality was assessed using the 5300 Bioanalyzer (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). Only high-quality RNA samples (OD260/280\u0026thinsp;=\u0026thinsp;1.8\u0026thinsp;~\u0026thinsp;2.2, OD260/230\u0026thinsp;\u0026ge;\u0026thinsp;2.0, RIN\u0026thinsp;\u0026ge;\u0026thinsp;6.5, 28S:18S\u0026thinsp;\u0026ge;\u0026thinsp;1.0, \u0026gt;\u0026thinsp;1\u0026micro;g) were utilized for constructing the sequencing library.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Library preparation and Sequencing\u003c/h2\u003e \u003cp\u003eRNA purification, reverse transcription, library construction, and sequencing were conducted at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China) following the manufacturer\u0026rsquo;s instructions (Illumina, San Diego, CA). The uterine endometrial tissue RNA-seq transcriptome library was prepared using Illumina\u0026reg; Stranded mRNA Prep, Ligation from Illumina (San Diego, CA) with 1\u0026micro;g of total RNA. Initially, messenger RNA was isolated via the polyA selection method using oligo(dT) beads and subsequently fragmented using a fragmentation buffer. Next, double-stranded cDNA was synthesized employing the SuperScript double-stranded cDNA synthesis kit (Invitrogen, CA) with random hexamer primers (Illumina). The synthesized cDNA underwent end-repair, phosphorylation, and 'A' base addition according to Illumina's library construction protocol. Libraries were size-selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose, followed by PCR amplification using Phusion DNA polymerase (NEB) for 15 PCR cycles. After quantification using Qubit 4.0, the paired-end RNA-seq sequencing library was sequenced using the NovaSeq 6000 sequencer (2 \u0026times; 150 bp read length). The raw paired end reads were trimmed and quality controlled by fastp\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e with default parameters. Then clean reads were separately aligned to reference genome with orientation mode using HISAT\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e software. The mapped reads of each sample were assembled by StringTie\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e in a reference-based approach (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mRNA sequencing data statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaw bases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClean reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClean bases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eError rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ20\u003c/p\u003e \u003cp\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ30\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47350426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7149914326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46067584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6758894916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54298556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8199081956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52713700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7623789224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56356580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8509843580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54883286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7950335261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43141658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6514390358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41434754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6078093406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53446106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8070362006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51813696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7511173014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53016154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8005439254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51276054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7392516867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLETB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48829076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7373190476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47420486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6915852500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51965434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7846780534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49786886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7289164008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46722478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7055094178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45312204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6627150568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48005458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7248824158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46989584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6893496419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55793572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8424829372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53772540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7800850282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49896196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7534325596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48599792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7113620448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50342790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7601761290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48473942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7093253613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46850508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7074426708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45472326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6663625083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55172404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8331033004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53863158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7920817759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Control Group (CG); Latent Endometrial Tuberculosis group (LETB); Inflammatory group (IG); Non-Inflammatory (NIG) group; Tuberculosis group (TB); The current diagnostic criteria consider a positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA), indicative of LETB suspicion, and positivity for both CD38 and CD138, suggesting inflammation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Differential expression analysis and Functional enrichment\u003c/h2\u003e \u003cp\u003eTo identify differentially expressed genes (DEGs) between two distinct samples, transcript expression levels were quantified using the transcripts per million reads (TPM) method. Gene abundances were quantified using RSEM\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Differential expression analysis was conducted using either DESeq2 or DEGseq.\u0026nbsp;DEGs meeting the criteria of |log2FC| \u0026ge; 1 and FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05 (DESeq2)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e or FDR\u0026thinsp;\u0026le;\u0026thinsp;0.001 (DEGseq)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e were considered significantly differentially expressed. Additionally, functional enrichment analysis, encompassing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, was performed to identify DEGs significantly enriched in GO terms and metabolic pathways, with a Bonferroni-corrected P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 compared to the whole-transcriptome background. GO functional enrichment and KEGG pathway analysis were conducted using Goatools and KOBAS\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eIn this study, we conducted single-gene gene set enrichment analysis (GSEA) utilizing R programming language and its packages, specifically 'limma' for differential gene expression analysis, 'ggplot2' and 'pheatmap' for data visualization, 'clusterProfiler' and 'enrichplot' for enrichment analysis against databases such as KEGG, 'org.Hs.eg.db' for gene identifier conversion, and 'patchwork' along with 'gseaplot2' for the visualization of the enrichment results. This approach allowed us to delve into the enrichment of the gene sets associated with the gene of interest within immune-related biological pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Quantitative Real-Time PCR (qPCR) Analysis\u003c/h2\u003e \u003cp\u003eIn this study, we employed quantitative real-time polymerase chain reaction (qPCR) technology to perform relative quantification analysis of gene expression in tissue samples. The experimental workflow included RNA extraction (using the OmniPlant RNA Kit from Beijing Kangwei Century Biotechnology Co., Ltd.), reverse transcription (with the HiScript Q RT SuperMix for qPCR from Nanjing Novozymes Biotechnology Co., Ltd.), and relative quantification of target genes versus reference genes (β-actin and GAPDH) using the ChamQ SYBR Color qPCR Master Mix (also from Nanjing Novozymes Biotechnology Co., Ltd.). Amplification and data acquisition were conducted using the LineGene9600plus real-time quantitative PCR instrument (Hangzhou Bioer Technology Co., Ltd.). Primers were synthesized with PAGE purification by Shenggong Bioengineering Co., Ltd., ensuring specificity and efficiency (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of qPCR Primers Required for Gene Validation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBase Sequence (5' to 3')\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essIFI30-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTGACCCTCTACTATGAAGCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essIFI31-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGCACTTGAACTCCCACC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essHCK-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAGGCAATACATTCTCAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essHCK-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATACAGGGCAACCACGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essSP1-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACTGGAGGTGTCTGACGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essSP1-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCTTGGACGAGAACTGGAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essIL1B-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTCGCCAGTGAAATGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essIL1B-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGCCCTTGCTGTAGTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essITGB2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCTCACCCTGTGGCAAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essITGB2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCTCCAGCGTGTAGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essFCGR2A-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTCCATCCCACAAGCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essFCGR2A-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGTCGCAATGACCACA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical significance was determined by one-way ANOVA using SPSS software (SPSS 21.0 for Windows). All productionrelated data are reported as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE, and differences were considered significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Absence of reliable diagnostic markers for Latent Endometrial Tuberculosis (LETB)\u003c/h2\u003e \u003cp\u003eThe endometrium often accompanies inflammation during the immune response against latent tuberculosis. Our study aimed to explore reliable inflammatory biomarkers for LETB diagnosis. Initially, we investigated pregnancy outcomes by comparing the Control Group (CG) and LETB, using the following criteria for grouping. Current diagnostic standards utilized positive Tuberculosis Interferon-Gamma Release Assay (TB-IGRA) results as indicative of LETB, along with positivity for CD38 and CD138, suggesting inflammation. The results revealed no significant differences in pregnancy outcomes between the Inflammatory (IG) and Non-Inflammatory (NIG) groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating the lack of specific diagnostic markers for LETB (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eSubsequently, we compared Hematoxylin and Eosin (HE) staining results between the control group and LETB, showing no significant differences. Immunohistochemistry (IHC) using CD38 and CD138 as inflammatory markers also showed no significant difference in inflammation levels between LETB and the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These findings further support the conclusion that LETB lacks specific inflammatory markers. Additionally, comparison of intrauterine ultrasound images between the control group and LETB showed no significant differences in endometrial morphology, thickness, or uterine cavity volume. Overall, our experimental results indicate the absence of specific inflammatory markers associated with LETB (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Transcriptomic Analysis Reveals Novel Inflammatory Biomarkers for LETB\u003c/h2\u003e \u003cp\u003eTranscriptomic analysis aimed to uncover novel inflammatory biomarkers associated with latent endometrial tuberculosis (LETB). Through a systematic screening process, a series of pairwise intersections were performed among the control group (CG), LETB, and tuberculosis group (TB), followed by a subsequent intersection of the three sets. This approach yielded intersection A, representing a common pool of differentially expressed genes shared across the groups, thus serving as potential diagnostic targets. Further refinement involved excluding interference from common inflammatory genes. By intersecting CG-NIG and CG-IG, intersection B, comprising common inflammatory genes, was identified. Subsequently, genes in intersection B were removed from gene set A to derive gene set C, which specifically captured LETB-related inflammatory biomarkers. Lastly, intersections between LETB-NIG and LETB-IG revealed a specific set of inflammatory genes induced by latent tuberculosis, constituting the final candidate gene set E (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The differential expression patterns of these potential biomarkers were visualized in a heatmap, showcasing downregulated genes in orange and upregulated genes in green across CG, LETB, and TB groups. Seven candidate genes, identified through rigorous screening, were highlighted in red (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Unveiling Distinct Immune Response Functional Enrichment and Protein Interaction Networks in LETB-Associated Candidate Genes\u003c/h2\u003e \u003cp\u003eIn our study, we conducted an in-depth analysis of candidate genes potentially indicative of LETB using comprehensive bioinformatics approaches. The results from Gene Ontology (GO) and KEGG pathway analyses provided detailed insights into the biological processes, cellular components, and molecular functions associated with these genes. GO analysis highlighted their pivotal role in immune response regulation, particularly in processes such as positive regulation of myeloid leukocyte-mediated immunity, leukocyte degranulation, and cell activation. Additionally, the cellular localization of these genes, including tertiary granule membrane, secretory granule membrane, and membrane rafts, suggests their involvement in intracellular signaling and material transport. At the molecular function level, the encoded proteins exhibited diverse functionalities crucial for cellular signaling, such as complement binding, protein tyrosine kinase activity, and STAT family protein binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eFurthermore, KEGG pathway analysis revealed associations between these genes and tuberculosis-related signaling pathways, shedding light on their roles in disease onset and progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Protein-protein interaction (PPI) analysis unveiled interactive relationships within cellular signaling networks. Notably, key interacting proteins identified, such as HCK, SPI1, IL1B, ITGB2, and FCGR2A, may play central roles in immune response and cellular signaling. These interactions may represent critical nodes in immune regulation processes. Integrating these findings, we identified candidate genes closely associated with LETB, including IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Validating Tuberculosis Genomic Markers: Bridging Transcriptomics with Clinical Application\u003c/h2\u003e \u003cp\u003eIn our study, we conducted a comprehensive analysis using Gene Set Enrichment Analysis (GSEA) to investigate the potential roles of six key genes (IF130, HCK, SPI1, IL1B, ITGB2, and FCGR2A) identified in endometrial tissues with latent tuberculosis infection, within immune-related biological pathways. Our analysis revealed significant enrichment of these genes in multiple pathways closely associated with immune response, including \u0026ldquo;KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION,\" \"KEGG_AXON_GUIDANCE,\u0026rdquo; \u0026ldquo;KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION,\u0026rdquo; and \"KEGG_CHEMOKINE_SIGNALING_PATHWAY.\"\u003c/p\u003e \u003cp\u003eThese pathways share a common theme in their core roles in communication, migration, and signal transduction among immune cells. Particularly, the enrichment of IF130, HCK, SPI1, IL1B, ITGB2, and FCGR2A in the \u0026ldquo;KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION\u0026rdquo; pathway suggests their potential involvement in regulating the interaction between cytokines and their receptors, a crucial step in immune response. Furthermore, the enrichment of ITGB2 in the \u0026ldquo;KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION\u0026rdquo; pathway implies its importance in the migration of immune cells across the endothelial layer, crucial for immune cell trafficking to sites of infection or inflammation. The enrichment of FCGR2A in the \"KEGG_CHEMOKINE_SIGNALING_PATHWAY\" pathway may be related to the chemotaxis and signaling mechanisms of immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the analysis, we validated potential LETB biomarkers through qPCR, HE staining, and immunohistochemistry. The qPCR results indicated that the relative expression of IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A was significantly higher in the inflammatory group (IG) compared to the non-inflammatory group (NIG) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). HE staining revealed notable inflammatory cell infiltration and structural alterations in the IG, whereas the NIG maintained relatively normal tissue architecture. Additionally, based on qPCR data, we selected HCK and ITGB2 for immunohistochemical validation, which demonstrated significantly enhanced expression in the IG. These findings collectively suggest that these biomarkers hold substantial clinical diagnostic potential in inflammatory conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study aimed to identify specific biomarkers for Latent Endometrial Tuberculosis (LETB) by employing RNA-Seq transcriptome profiling. Initial comparisons revealed that conventional diagnostic methods, including TB-IGRA and markers like CD38 and CD138, were insufficient in distinguishing LETB from control samples based on pregnancy outcomes, histological examinations, and immunohistochemistry (IHC) results. Subsequent transcriptomic analyses identified seven candidate genes (IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A) uniquely associated with LETB, which were further validated through quantitative PCR (qPCR) and IHC, confirming their differential expression in LETB tissues.\u003c/p\u003e \u003cp\u003eThe immune response in LETB is characterized by unique inflammatory markers that differentiate it from other forms of tuberculosis and from healthy control endometrial tissues. Our study found that genes such as IFI30 and SPI1 play significant roles in myeloid leukocyte-mediated immunity and leukocyte degranulation, indicating a robust immune response specific to LETB. Gene Ontology (GO) and KEGG pathway analyses revealed these genes' involvement in critical immune pathways, such as cytokine-cytokine receptor interaction and leukocyte transendothelial migration. These pathways are essential for immune cell communication, migration, and activation, which are crucial for mounting an effective response against latent infections.\u003c/p\u003e \u003cp\u003eThe six genes identified (IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A) have been implicated in various immune regulatory mechanisms and inflammation associated with tuberculosis. IFI30, known for its role in antigen processing and presentation, is critical in the immune system's ability to recognize and respond to Mycobacterium tuberculosis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. HCK and SPI1 are involved in the signaling pathways that activate macrophages and other immune cells, highlighting their importance in controlling TB infection\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. IL1B, a key pro-inflammatory cytokine, has been shown to be elevated in TB infections, contributing to the inflammatory milieu necessary for fighting the pathogen\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. ITGB2 and FCGR2A are integral to leukocyte adhesion and migration, facilitating the movement of immune cells to sites of infection\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBuilding on our findings, future research should focus on expanding sample sizes and integrating proteomic and metabolomic data to provide a more comprehensive understanding of LETB's impact on infertility. Longitudinal studies are needed to explore the progression of LETB and its direct effects on reproductive outcomes. Additionally, functional studies of the identified biomarkers will help elucidate their roles in LETB pathogenesis and pave the way for the development of targeted diagnostic tools and treatments, ultimately improving fertility outcomes for affected women.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identifies and validates novel inflammatory biomarkers for LETB, offering new insights into its pathogenesis and potential avenues for improved diagnostic and therapeutic strategies. The identified biomarkers, including IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A, hold promise for enhancing the early detection and treatment of LETB, thereby improving reproductive health outcomes for affected women.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eXiu-Juan Chen conceived and supervised the study, Bai Dai and\u0026nbsp;Zhi-min\u0026nbsp;Wang\u0026nbsp;designed and performed the experiments.Jing-ying Liu and\u0026nbsp;Xin Chen analyzed the data and wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the science and technology project\u0026nbsp;of\u0026nbsp;Inner Mongolia Autonomous Region, China\u0026nbsp;(2021GG0389), Natural Science Foundation of Inner Mongolia Autonomous Region, China (2023QN08038),\u0026nbsp;Inner Mongolia Medical University Youth Project (YKD2024QN004), Inner Mongolia Archives Management Science and Technology Project of Inner Mongolia Autonomous Region, China (2023-10), Inner Mongolia Medical University Laboratory Open Fund Project of the Inner Mongolia Autonomous Region, China (2023ZN04), research project of Inner Mongolia Medical University Affliated Hospital of the Inner Mongolia Autonomous Region, China (2023NYFYGGO14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We are grateful to the YiKang and BeiKang limited liability company for their support with the single cell sequencing experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChakaya, J.\u003cem\u003e et al.\u003c/em\u003e Global Tuberculosis Report 2020 - Reflections on the Global TB burden, treatment and prevention efforts. \u003cem\u003eInt J Infect Dis\u003c/em\u003e \u003cstrong\u003e113 Suppl 1\u003c/strong\u003e, S7-S12 (2021). https://doi.org:10.1016/j.ijid.2021.02.107\u003c/li\u003e\n\u003cli\u003eSharma, J. B., Sharma, E., Sharma, S. \u0026amp; Dharmendra, S. Female genital tuberculosis: Revisited. \u003cem\u003eIndian J Med Res\u003c/em\u003e \u003cstrong\u003e148\u003c/strong\u003e, S71-S83 (2018). https://doi.org:10.4103/ijmr.IJMR_648_18\u003c/li\u003e\n\u003cli\u003eGhosh, K., Ghosh, K. \u0026amp; Chowdhury, J. R. Tuberculosis and female reproductive health. \u003cem\u003eJ Postgrad Med\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 307-313 (2011). https://doi.org:10.4103/0022-3859.90082\u003c/li\u003e\n\u003cli\u003eFlynn, J. L. \u0026amp; Chan, J. Tuberculosis: latency and reactivation. \u003cem\u003eInfect Immun\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 4195-4201 (2001). https://doi.org:10.1128/IAI.69.7.4195-4201.2001\u003c/li\u003e\n\u003cli\u003eHouben, R. M. \u0026amp; Dodd, P. J. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. \u003cem\u003ePLoS Med\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e1002152 (2016). https://doi.org:10.1371/journal.pmed.1002152\u003c/li\u003e\n\u003cli\u003eMacLean, E.\u003cem\u003e et al.\u003c/em\u003e A systematic review of biomarkers to detect active tuberculosis. \u003cem\u003eNat Microbiol\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 748-758 (2019). https://doi.org:10.1038/s41564-019-0380-2\u003c/li\u003e\n\u003cli\u003eWallis, R. S., Peppard, T. \u0026amp; Hermann, D. Month 2 culture status and treatment duration as predictors of recurrence in pulmonary tuberculosis: model validation and update. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e0125403 (2015). https://doi.org:10.1371/journal.pone.0125403\u003c/li\u003e\n\u003cli\u003eBarry, C. E., 3rd\u003cem\u003e et al.\u003c/em\u003e The spectrum of latent tuberculosis: rethinking the biology and intervention strategies. \u003cem\u003eNat Rev Microbiol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 845-855 (2009). https://doi.org:10.1038/nrmicro2236\u003c/li\u003e\n\u003cli\u003eGoletti, D., Petruccioli, E., Joosten, S. A. \u0026amp; Ottenhoff, T. H. Tuberculosis Biomarkers: From Diagnosis to Protection. \u003cem\u003eInfect Dis Rep\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 6568 (2016). https://doi.org:10.4081/idr.2016.6568\u003c/li\u003e\n\u003cli\u003eChen, S., Zhou, Y., Chen, Y. \u0026amp; Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, i884-i890 (2018). https://doi.org:10.1093/bioinformatics/bty560\u003c/li\u003e\n\u003cli\u003eKim, D., Langmead, B. \u0026amp; Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 357-360 (2015). https://doi.org:10.1038/nmeth.3317\u003c/li\u003e\n\u003cli\u003ePertea, M.\u003cem\u003e et al.\u003c/em\u003e StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. \u003cem\u003eNat Biotechnol\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 290-295 (2015). https://doi.org:10.1038/nbt.3122\u003c/li\u003e\n\u003cli\u003eLi, B. \u0026amp; Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 323 (2011). https://doi.org:10.1186/1471-2105-12-323\u003c/li\u003e\n\u003cli\u003eLove, M. I., Huber, W. \u0026amp; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 550 (2014). https://doi.org:10.1186/s13059-014-0550-8\u003c/li\u003e\n\u003cli\u003eWang, L., Feng, Z., Wang, X., Wang, X. \u0026amp; Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 136-138 (2010). https://doi.org:10.1093/bioinformatics/btp612\u003c/li\u003e\n\u003cli\u003eXie, C.\u003cem\u003e et al.\u003c/em\u003e KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, W316-322 (2011). https://doi.org:10.1093/nar/gkr483\u003c/li\u003e\n\u003cli\u003ePanda, S.\u003cem\u003e et al.\u003c/em\u003e Identification of differentially recognized T cell epitopes in the spectrum of tuberculosis infection. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 765 (2024). https://doi.org:10.1038/s41467-024-45058-9\u003c/li\u003e\n\u003cli\u003eMai, D.\u003cem\u003e et al.\u003c/em\u003e Exposure to Mycobacterium remodels alveolar macrophages and the early innate response to Mycobacterium tuberculosis infection. \u003cem\u003ePLoS Pathog\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, e1011871 (2024). https://doi.org:10.1371/journal.ppat.1011871\u003c/li\u003e\n\u003cli\u003eYin, J.\u003cem\u003e et al.\u003c/em\u003e Common variants of pro-inflammatory gene IL1B and interactions with PPP1R13L and POLR1G in relation to lung cancer among Northeast Chinese. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 7352 (2023). https://doi.org:10.1038/s41598-023-34069-z\u003c/li\u003e\n\u003cli\u003eBlazevic, A.\u003cem\u003e et al.\u003c/em\u003e Phase 1 Open-Label Dose Escalation Trial for the Development of a Human Bacillus Calmette-Guerin Challenge Model for Assessment of Tuberculosis Immunity In Vivo. \u003cem\u003eJ Infect Dis\u003c/em\u003e \u003cstrong\u003e229\u003c/strong\u003e, 1498-1508 (2024). https://doi.org:10.1093/infdis/jiad441\u003c/li\u003e\n\u003cli\u003eLiu, Y.\u003cem\u003e et al.\u003c/em\u003e DNA methylation of ITGB2 contributes to allopurinol hypersensitivity. \u003cem\u003eClin Immunol\u003c/em\u003e \u003cstrong\u003e248\u003c/strong\u003e, 109250 (2023). https://doi.org:10.1016/j.clim.2023.109250\u003c/li\u003e\n\u003cli\u003eShi, X., Ma, Y., Li, H. \u0026amp; Yu, H. Association between FCGR2A rs1801274 and MUC5B rs35705950 variations and pneumonia susceptibility. \u003cem\u003eBMC Med Genet\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 71 (2020). https://doi.org:10.1186/s12881-020-01005-1\u003c/li\u003e\n\u003cli\u003eLu, S.\u003cem\u003e et al.\u003c/em\u003e Fc fragment of immunoglobulin G receptor IIa (FCGR2A) as a new potential prognostic biomarker of esophageal squamous cell carcinoma. \u003cem\u003eChin Med J (Engl)\u003c/em\u003e \u003cstrong\u003e135\u003c/strong\u003e, 482-484 (2021). https://doi.org:10.1097/CM9.0000000000001776\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Latent Endometrial Tuberculosis, Inflammatory Biomarkers, RNA-Seq, Immune Response, Diagnosis, Female Infertility, Transcriptomic Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5254793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5254793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLatent Endometrial Tuberculosis (LETB) is a significant yet under-recognized cause of female infertility, particularly in TB-prevalent regions. Current diagnostic methods for LETB lack specificity, complicating early detection. Through RNA-Seq transcriptome profiling, we aimed to uncover distinct immune response landscapes and identify novel inflammation-related diagnostic markers for LETB. Our study included clinical diagnostics, histological examinations, and transcriptomic analyses comparing differentially expressed genes (DEGs) among control, LETB, and active TB groups. We identified seven candidate genes (IFI30, HCK, SPI1, IL1B, ITGB2, and FCGR2A) uniquely associated with LETB. Bioinformatic analyses revealed these genes' significant roles in immune regulation, including leukocyte activation, cytokine signaling, and myeloid leukocyte-mediated immunity. Gene Set Enrichment Analysis (GSEA) confirmed their involvement in key immune pathways such as cytokine-cytokine receptor interaction and leukocyte transendothelial migration. Validation through qPCR and immunohistochemistry confirmed the differential expression of these biomarkers in LETB tissues. These findings provide new insights into LETB pathogenesis, suggesting potential biomarkers for enhanced early diagnosis and treatment, ultimately aiming to improve reproductive health outcomes for affected women.\u003c/p\u003e","manuscriptTitle":"Uncovering Immune Response Landscapes and Novel Biomarkers in Latent Endometrial Tuberculosis: Insights from RNA-Seq Transcriptome Profiling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 18:13:52","doi":"10.21203/rs.3.rs-5254793/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-10T10:59:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-09T09:26:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168615041544914571589456648083890286542","date":"2024-12-09T01:36:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-11T14:24:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216241037593807938977888495854318651365","date":"2024-11-09T05:24:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55173615599356969165646404798881960064","date":"2024-11-09T02:24:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-07T01:08:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-07T01:03:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-04T14:51:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-01T05:34:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-13T09:58:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f67f4eb-0ed7-40bd-b1a5-446aeb7f6894","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:13:46+00:00","versionOfRecord":{"articleIdentity":"rs-5254793","link":"https://doi.org/10.1038/s41598-025-89483-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-10 16:05:45","publishedOnDateReadable":"April 10th, 2025"},"versionCreatedAt":"2024-11-13 18:13:52","video":"","vorDoi":"10.1038/s41598-025-89483-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-89483-2","workflowStages":[]},"version":"v1","identity":"rs-5254793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5254793","identity":"rs-5254793","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}