Systemic Inflammation and Hyperprolactinemia as Determinants of Progesterone Receptor Gene Expression in Recurrent Pregnancy Loss with Endometriosis

& Objective: Recurrent Pregnancy Loss (RPL) in women with endometriosis involves immune and hormonal dysregulation affecting Progesterone Receptor (PGR) signaling. Systemic inflammation and elevated prolactin may impair this pathway and contribute to pregnancy failure. This study was conducted with aim to evaluate the association of serum Interleukin-6 (IL -6), prolactin, and Progesterone Receptor (PGR) gene expression, and their diagnostic potential.

& Methods: This case-control study was conducted on 250 women (125 with RPL and endometriosis; 125 controls). Serum IL -6 and prolactin were measured by ELISA, and PGR mRNA expression was quantified using real -time PCR. Group comparisons were performed using non -parametric test s. Correlation, logistic regression, and ROC analyses were applied. P<0.05 was considered statistically significant.

IL-6 and prolactin levels were significantly higher, while PGR expression was significantly lower in cases compared to controls (P<0.001). PGR expression showed the highest diagnostic performance, followed by prolactin and IL-6. IL-6 and prolactin were negatively associated with PGR expression, and prolactin remained an independent predictor after adjustment.

These findings demonstrate that inflammatory and hormonal alterations are associated with reduced progesterone receptor expression, indicating their potential role in RPL among women with endometriosis.

Recurrent Pregnancy loss, Endometriosis, Progesterone receptor gene expression, Interleukin -6, Prolactin Received: 2026/02/17 Accepted: 2026/04/28 Published Online: 18 Jul. 2026 Corresponding Information: Boopathi Kanniappan, Department of Plastic Surgery, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research (Deemed to be University) Kanchipuram, Tamil Nadu, India Email: [email protected] Dinesh Roy Divakaran, CEO & Senior Cytogeneticist Genetika, Centre for Advanced Genetic Studies, Thiruvananthapuram, Kerala, India Email: [email protected] Copyright © 2026, This is an original open-access article distributed under the terms of the Creative Commons Attribution-noncommercial 4.0 International License which permits copy and redistribution of the material just in noncommercial usages with proper citation . 1. Introduction Recurrent Pregnancy Loss (RPL), defined by ESHRE (2023) as two or more losses before 24 weeks of gestation, affects approximately 2 -5% of women of reproductive age and remains a multifactorial clinical challenge involving genetic, hormonal, anatomical, immunological, and inflammatory factors (1). Despite thorough evaluation, many RPL cases remain unexplained, suggesting a role for endocrine -immune interactions. Endometriosis, an estrogen -dependent inflammatory condition, is increasingly recognized in these women and may impair implantation, disrupt endometrial receptivity, and contribute to early 821 Prolactin and PGR Expression in Endometriosis RPL Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research pregnancy loss (2,3). RPL with endometriosis represents a complex subgroup where hormonal dysregulation and chronic inflammation interact to worsen reproductive dysfunction. Progesterone is essential for pregnancy establishment and maintenance, acting via the Progesterone Receptor (PGR) gene, which encodes a ligand-activated transcription factor on chromosome 11q22.1 (3). It regulates endometrial receptivity, decidualization, trophoblast -maternal communication, and immune tolerance at the maternal -fetal interface through genomic signaling (4). Endometrial responsiveness depends on the PR -A/PR-B isoform balance, with dysregulation linked to progesterone resistance in endometriosis and RPL (5,6). Progesterone receptors are also expressed in immune cells, where they regulate cytokine production and differentiation; thus, peripheral PGR expression may reflect systemic progesterone -mediated immune signaling rather than local endometrial receptor density. In women with coexisting RPL and endometriosis, altered PGR expression may reflect systemic immuno- endocrine dysregulation driven by interacting inflammatory and hormonal pathways. Reduced peripheral PGR may indicate impaired progesterone - mediated immunore gulation and an adverse inflammatory reproductive phenotype. Chronic inflammation, particularly Interleukin-6 (IL-6), plays a central role in endometriosis and reproductive failure by promoting aberrant immune responses, lesion proliferation, angiogenesis, immune recruitment, and progesterone resistance (7). Although IL-6 findings in RPL are inconsistent (8), persistent IL -6 driven inflammation in coexisting RPL and endometriosis may impair immune tolerance, creating a pro - inflammatory, less receptive, and progesterone - resistant uterine environment that increases pregnancy loss risk. Hyperprolactinemia, a common endocrine abnormality, may impair endometrial receptivity by disrupting the hypothalamic -pituitary-gonadal axis, reducing luteal function and progesterone synthesis (9). Prolactin also exerts immunomodulatory effects, influencing T -cell activation and cytokine release, potentially exacerbating endometrial inflammation. Emerging evidence suggests it may alter PGR gene expression, compounding inflammatory effects in women with endometriosis and RPL. Although inflammation, hyperprolactinemia, and progesterone resistance are individually linked to reproductive failure, their combined effect on PGR gene expression in women with RPL and endometriosis remains unclear. This study was conducted with aim to e xamines the association of inflammatory and prolactin alterations with PGR expression, aiming to clarify underlying immuno - endocrine mechanisms and identify potential diagnostic markers. 2. Materials and Methods This case -control study was conducted on 250 women aged 25 -45 years (125 with RPL and endometriosis; 125 healthy controls) in 2023 -2025. Written informed consent was obtained, and demographic, lifestyle, and clinical data were collected using a structured questionnaire. Clinical evaluation, sample collection, and laboratory analyses were conducted under standardized conditions in collaboration with multiple centers. Controls were recruited from community -based health screening programs. Inclusion criteria for case group were women aged 25-45 years with clinically confirmed Recurrent Pregnancy Loss (RPL), defined as two or more consecutive losses before 24 weeks of gestation (ESHRE guidelines), and coexisting endometriosis. Endometriosis was diagnosed using standard criteria, including transvaginal ultrasonography with confirmation by laparoscopy and /or histopathology where available. Consistent inclusion criteria were applied to minimize clinical heterogeneity. Age-matched fertile women aged 25 -45 years with no history of recurrent pregnancy loss or evidence of endometriosis were included as controls. Fertility was confirmed by at least one prior full-term pregnancy. All controls had regular menstrual cycles (21-35 days) and met the same exclusion criteria as cases. Participants were excluded if they had autoimmune or unrelated endocrine disorders, uterine structural abnormalities, active pelvic infections, malignancy, or chronic systemic diseases. Those who had received hormonal or immunomodulatory therapy within the previous three months or had incomplete clinical data were also excluded. Sample size was calculated using the formula Z²pq/d², where Z is the standard normal deviation, p the estimated prevalence from published data, q=1−p, and d the margin of error. The prevalence was derived from existing literature to ensure an adequate sample size. All participants provided information on their demographics, medical history, and lifestyle through a structured questionnaire. Their height, weight, and waist circumference were measured using standardized procedures. Fasting venous blood (8-10 mL) was collected under aseptic conditions into plain tubes for inflammatory and hormonal assays and EDTA tubes for molecular analysis. Samples were processed within 60 minutes. Plain tubes were allowed to clot (15 -30 minutes, room temperature) and centrifuged at 3000 rpm for 10 minutes to obtain serum, which was stored at -20°C until analysis. RNA extraction was performed from EDTA samples under RNase-free conditions. Serum Interleukin-6 (IL -6) levels were quantified using a commercial sandwich ELISA kit (Cat. No. Bobby Joseph, et al. 822 Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research OPK1156; Origin Diagnostics and Research) according to the manufacturer’s protocol. Standards and serum samples were added to pre -coated wells, followed by incubation with biotinylated detection antibody and streptavidin -HRP. After washing, TMB substrate was added, and the reaction was stopped. Absorbance was measured at 450 nm, and concentrations were calculated from a standard curve.

were expressed in pg/mL. Serum Prolactin levels were measured using a commercially available sandwich ELISA kit (Category No. OPK1224) manufactured by Origin Diagnostics and Research. The assay was performed according to the manufacturer’s protocol. Absorbance was read at 450 nm u sing a microplate reader, and prolactin concentrations were determined from the standard calibration curve. Results were expressed in ng/mL. To minimize physiological variation in prolactin levels, blood samples were collected under standardized conditions, preferably in the morning hours following an overnight fast. Efforts were made to ensure consistency in the timing of sample collection across participants. Participants with known endocrine disorders, including thyroid dysfunction and pituitary abnormalities, were excluded to reduce potential confounding effects on prolactin levels. Molecular Analysis RNA Isolation and cDNA synthesis. Total RNA was extracted using an RNA isolation kit (Origin Diagnostics and Research; Cat. No. OPD419) based on guanidine thiocyanate -phenol-chloroform extraction with spin -column purification. Samples were homogenized in Buffer RZ to ensure complete lysis, and for blood samples, three volumes of Buffer RZ were added and mixed thoroughly. Following homogenization, chloroform (200 µL per 1 mL Buffer RZ) was added, mixed vigorously, and incubated for 3 minutes at room temperature. Samples were centrifuged at 12,000 rpm for 10 minutes at 4°C to separate phases. The aqueous phase was transferre d to a new tube, mixed with an equal volume of ethanol (50–100%), and loaded onto an RNase -free spin column for RNA binding. The column was washed with Buffer RW and Buffer RZ, followed by centrifugation to remove residual ethanol. RNA was eluted in 30 µL RNase-free water by centrifugation at 12,000 rpm for 2 minutes. RNA purity was assessed using an Eppendorf BioSpectrometer based on the A260/A280 ratio, with values of 1.8 -2.0 considered acceptable. To minimize degradation, RNA was immediately reverse - transcribed into cDNA under RNase-free conditions. Samples meeting quality criteria were subsequently reverse-transcribed into Complementary DNA (cDNA) using a cDNA synthesis kit from the same manufacturer (Cat. No. ODR41), employing a blend of oligo (dT)₁₈ and random hexamer primers with reverse transcriptase, under the manufacturer’s recommended reaction conditions. The synthesized cDNA was aliquoted and stored at -20°C until downstream gene expression analysis Quantitative Real-Time PCR (qRT-PCR) PGR gene expression was quantified using SYBR Green-based qRT -PCR with specific primers (Forward: 5′ -GTCGCCTTAGAAAGTGCTGTCAG- 3′; Reverse: 5′-GCTTGGCTTTCATTTGGAACGCC- 3′). GAPDH was used as the reference gene due to its stable expression in peripheral blood leukocytes and validated use in gene expression studies. Gene expression was normalized to GAPDH to control for variation in RNA input and cDNA synthesis. Relative expression was calculated using the comparative Ct (ΔΔCt) method, where ΔCt=Ct(PGR) - Ct(GAPDH), and ΔΔCt was derived relative to the control group. Fold change was expressed as 2^−ΔΔCt. RNA extraction, cDNA synthesis, and PCR setup were performed in separate designated areas to prevent contamination. All steps were conducted in accordance with Good Laboratory Practice (GLP) guidelines. PGR primers (23 bp) were procured from Eurofins Genomics India Pvt. Ltd. and validated in silico using NCBI Primer-BLAST to ensure specific amplification. qRT-PCR was performed in a 20 μL reaction containing SYBR Green 2X Master Mix, primers, cDNA, and nuclease -free water. Thermal cycling included initial denaturation at 95°C for 5 minutes, followed by 30-40 cycles of 94°C (1 minute), 60°C (1 minute), and 72°C (1 minute), with a final extension at 72°C for 10 minutes and melt curve analysis. Gene expression was normalized to a housekeeping gene and calculated using the comparative Ct method. Data were analyzed using Stata 17.0. Normality of data was assessed using the Shapiro -Wilk test. Non - normally distributed variables were expressed as median (IQR) and compared using the Mann -Whitney U test. Associations between IL -6, prolactin, PGR expression, and case status were evaluated using binary logistic regression. Correlations were assessed using Spearman’s rank test. ROC curve analysis was performed to evaluate discriminatory abilit y, with AUC (95% CI) calculated and optimal cut -off values determined using the Youden Index. P<0.05 was considered statistically significant. 3. Results A total of 250 women were enrolled in this study. Among the study population, 125 cases were clinically diagnosed with Recurrent Pregnancy Loss (RPL) with coexisting endometriosis, and 125 age -matched women without a history of RPL or endometriosis served as controls. Table 1 shows that IL-6 and prolactin levels were not normally distributed in either cases or controls (all P<0.05). PGR gene expression was also non -normally 823 Prolactin and PGR Expression in Endometriosis RPL Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research distributed in cases ( P<0.01), but followed an approximately normal distribution in controls (P=0.077). Overall, most variables violated the assumption of normality. Data represent P-values from the Shapiro-Wilk test. Statistical significance was set at P<0.05 Table 2 shows that cases had significantly higher serum IL-6 and prolactin levels compared to controls (P<0.01), indicating increased inflammatory activity and hormonal dysregulation. In contrast, progesterone receptor gene expression was significantly reduced in cases ( P<0.01), suggesting impaired progesterone signaling in cases. Receiver Operating Characteristic (ROC) analysis demonstrated good diagnostic performance for all three markers. The PGR gene showed the highest discriminative ability (AUC=0.870), followed by Prolactin (AUC=0.846) and IL -6 (AUC=0.838), indicating strong accuracy in distinguishing cases from controls. All AUC values were statistically significant (P<0.01). At the optimal cut -off values (Prolactin ≥18.4, IL -6≥6.2 pg /mL, PGR≤0.80), PGR achieved 100% specificity and positive predictive value, meaning it perfectly identified non-cases and produced no false positives in this sample (LR+ =∞). IL -6 demonstrated the highest sensitivity –specificity balance after PGR, with strong specificity (88%) and a high positive likelihood ratio (6.0). Prolactin also showed good overall diagnostic accuracy with balanced sensitivity (76%) and specificity (80%). Overall, PGR appears to be the strongest diagnostic marker among the three, wit h IL-6 and prolactin also showing clinically meaningful discriminative performance. Figure 1 and 2 summarizes the diagnostic performance of inflammatory, hormonal parameters and receptor gene biomarkers in differentiating cases from controls using receiver operating characteristic analysis. IL -6 exhibits good diagnostic accuracy (AUC=0.838) (Table 3, Fig 1), as reflected by its ROC curve lying well above the reference line. This indicates meaningful sensitivity and specificity in distinguishing cases from controls. The result reinforces the role of systemic inflammation as a significant contributor to disea se pathophysiology. Progesterone receptor gene expression demonstrates strong discriminatory performance (AUC=0.870) (Table 3, Fig 2), with its ROC curve positioned close to the upper -left corner. This indicates high sensitivity and specificity in differentiating cases from controls. The finding suggests that reduced PGR gene expression is a robust molecular marker and supports the concept of impaired progesterone signaling or progesterone resistance in the affected group. Prolactin shows strong discriminatory capability (AUC=0.846) (Table 3, Fig 2), with a ROC curve approaching the upper-left region. This suggests that elevated prolactin levels effectively differentiate affected women from controls, highlighting altered endocrine regulation as an important component of the underlying condition. The correlation coefficient ( P= −0.3729) shown in figure 3 indicates a weak negative and statistically significant association between prolactin concentration and progesterone receptor gene expression. The points show a gentle downward tendency rather than a steep fall. Higher prolactin values are more frequently accompanied by lower receptor expression, but the scatter remains relatively broad, reflecting the weaker magnitude of the association. Inflammation demonstrates a more pronounced effect. The association between IL -6 and progesterone receptor gene expression is moderate -to-strong in the negative direction and statistically significant ( P= −0.5437). Figure 4 thus indicates that increasing inflammatory burden may substantially suppress progesterone receptor gene activity. This finding points toward a possible inflammatory mechanism that could interfere with hormonal signaling and contribute to case status. Firth penalized logistic regression ( Table 4) showed a strong inverse association between peripheral PGR expression and case status. In the unadjusted model, reduced PGR expression demonstrated near -complete separation (OR=0.0003, P<0.01). After adjustment for IL-6 and prolactin, the association attenuated and became borderline significant (OR=0.004, P=0.057), indicating partial influence of inflammatory and endocrine factors. Prolactin remained an independent predictor (OR=1.110, P=0.005), whereas IL-6 was not significant. These findings suggest that hyperprolactinemia independently contributes to Table 1. Shapiro-Wilk test for normality of biochemical, hormonal and gene expression parameters among cases and controls Shapiro–Wilk p-value Variable Cases Controls IL-6 (pg/mL) <0.01 <0.01 Prolactin (ng/mL) 0.002 <0.01 Progesterone receptor gene expression <0.01 0.077 Bobby Joseph, et al. 824 Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research disease risk, while reduced PGR expression reflects systemic receptor dysregulation influenced by the inflammatory-hormonal milieu. Table 2. Comparison of inflammatory, hormonal and gene expression parameters between cases and controls Parameter Cases (n = 125) Median (IQR) Controls (n =125) Median (IQR) P-value IL-6 (pg/mL) 9.5(5.3-15.0) 3.2(1.4-4.7) <0.001* Prolactin (ng/mL) 28.4(22.1-35.6) 14.7(10.3-19.8) <0.001* PGR expression (2-ΔΔCt) 0.6(0.3-0.9) 1.0(1.0-1.1) <0.001* Data are presented as median (interquartile range, IQR). Between-group comparisons were performed using the Mann-Whitney U test due to non-normal distribution (Shapiro-Wilk P<0.05). A two-tailed P-value<0.05 was considered statistically significant. AUC=Area Under the Receiver Operating Characteristic Curve; CI=Confidence Interval; LR+ =Positive Likelihood Ratio; LR – =Negative Likelihood Ratio; PPV=Positive Predictive Value; NPV=Negative Predictive Value. Optimal cut -off values were determined using R OC curve analysis. An AUC>0.5 indicates discriminatory ability. LR+ =Sensitivity/(1 -Specificity); LR – =(1-Sensitivity)/ Specificity. An infinite LR+ indicates perfect specificity (no false positives) in the study sample Table 3. Diagnostic performance of hormonal and receptor biomarkers in distinguishing cases from controls Metric Prolactin IL-6 (pg/mL) PGR gene Area under curve (AUC) 0.846 0.838 0.870 95% CI 0.797-0.894 0.788-0.889 0.825-0.915 P-value (AUC > 0.5) <0.01 <0.01 <0.01 Optimal cut-off ≥18.4 ≥6.2 ≤0.80 Sensitivity (%) 76.0 72.0 70.0 Specificity (%) 80.0 88.0 100.0 Positive likelihood ratio (LR+) 3.8 6.0 ∞ Negative likelihood ratio (LR–) 0.30 0.32 0.30 Positive predictive value (%) 79.2 85.7 100.0 Negative predictive value (%) 76.9 75.9 77.2 825 Prolactin and PGR Expression in Endometriosis RPL Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research Table 4. Firth penalized logistic regression showing association between progesterone receptor gene expression and case-control status Predictor Model 1 OR 95% CI P-value Model 2 OR 95% CI P-value Progesterone receptor gene expression 0.0003 0.000-0.003 <0.01 0.004 0.000-1.181 0.057 IL-6 1.228 0.893-1.689 0.207 Prolactin 1.110 1.031-1.194 0.005 Figure 1. Receiver operating characteristic (ROC) curves showing the diagnostic performance of inflammatory marker (IL-6) ROC curves showing the diagnostic performance of Interleukin-6 (IL-6), Fasting Blood Sugar (FBS), serum total cholesterol, triglycerides, Low- Density Lipoprotein (LDL), and High-Density Lipoprotein (HDL) in distinguishing cases from controls. Sensitivity is plotted against 1- specificity. The reference diagonal line represents no discriminatory ability (AUC=0.5). Higher curve deviation toward the upper left corn er indicates better diagnostic performance. (n=125) Figure 2 . Receiver Operating Characteristic (ROC) curves showing the diagnostic performance of hormonal, and progesterone receptor gene expression markers (Prolactin and PGR Gene Expression) ROC curves illustrating the diagnostic performance of Thyroid hormones (T3, T4, TSH), Luteinizing Hormone (LH), Follicle-Stimulating Hormone (FSH), prolactin, estrogen, progesterone, and Progesterone Receptor (PGR) gene expression in distinguishing cases from controls. Sensitivity is plotted against 1- specificity. The reference diagonal line represents no discriminatory ability (AUC=0.5), with curves closer to the upper left corner indicating better diagnostic performance. (n=125) Bobby Joseph, et al. 826 Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research Figure 3. Relationship between serum prolactin levels and progesterone receptor gene expression among cases Scatter plot illustrates the relationship between serum prolactin levels (ng/mL) and Progesterone Receptor (PGR) gene expression. A negative correlation was observed (Spearman’s P= −0.3729, P<0.01), indicating that higher prolactin levels are associated with reduced PGR expression. The red line represents the fitted regression trend (n=125). Figure 4. Relationship between serum Interleukin-6 (IL-6) levels and progesterone receptor gene expression among cases Scatter plot showing the relationship between serum Interleukin-6 (IL-6) levels (pg/mL) and Progesterone Receptor (PGR) gene expression. A significant negative correlation was observed (Spearman’s P= −0.5437, P<0.01), indicating that higher IL -6 levels are associated with reduced PGR expression. The red line represents the fitted regression trend (n=125). 4. Discussion To the best of our knowledge, this is the first study which evaluated the combined effects of IL-6 mediated inflammation and hyperprolactinemia on PGR gene expression (mRNA, qRT -PCR) in a case -control cohort of women with RPL and endometriosis. Previous studies have examined progesterone resistance, cytokines, or prolactin separately, often in RPL or endometriosis alone. By integrating these factors in a dual -pathology subgroup, this study addresses a key gap and highlights interactions underlying impaired progesterone signaling. Serum IL-6 levels were significantly higher in cases than controls, indicating increased systemic inflammation. This aligns with evidence linking IL -6 to immune signaling at the maternal-fetal interface (10) and inflammatory processes in endometriosis -related infertility (11). While findings in RPL alone are 827 Prolactin and PGR Expression in Endometriosis RPL Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research inconsistent (12), the coexistence of RPL and endometriosis may represent a distinct inflammation - associated phenotype. Although IL -6 showed good discriminatory performance (AUC=0.846), it was not independently significant in adjusted Firth logistic regression, suggesting overlap with other factors. Unlike prior studies assessing IL -6 in isolation, this study evaluates inflammatory burden alongside PGR gene expression within a multivariable framework, providing insight into interactions between inflammatory, endocrine, and molecular parameters in this combined phenotype (13). Prolactin levels were significantly higher in cases than controls, indicating hyperprolactinemia. Elevated prolactin is associated with reproductive dysfunction through disruption of the hypothalamic -pituitary- ovarian axis, impaired luteal function, and reduced progesterone production, consistent with previous reports (11). In this study, prolactin showed strong discriminatory performance on ROC analysis and a weak but significant negative correlation with PGR expression. Importantly, it remained independently associated with case status in adjusted Firth logistic regression, suggesting an independent role rather than a secondary effect. In the context of systemic inflammation, elevated prolactin may be linked to altered progesterone signaling, reflecting coordinated endocrine–immune interactions. This integrated perspective extends prior research that has largely evaluated hyperprolactinemia in isolation. Progesterone Receptor (PGR) gene expression was significantly lower in our cases than controls, indicating altered progesterone signaling at a systemic level in women with RPL and endometriosis. Notably, progesterone receptor isoforms PR -A and PR -B have distinct roles, with PR -B mediating transcriptional activation and PR -A modulating PR -B activity; imbalance between these isoforms has been implicated in progesterone resistance in endometriosis and RPL. While prior studies have reported reduced receptor expression in endometrios is and altered receptor dynamics in RPL (14), these primarily focused on endometrial tissue. In contrast, this study assessed PGR mRNA in peripheral blood, providing insight into systemic regulation. PGR expression showed strong discriminatory ability in ROC analysis. In unadjusted Firth logistic regression, it was strongly associated with case status, but this association attenuated after adjustment, suggesting influence from upstream inflammatory and hormonal factors. These findings support an integrated model in which progesterone resistance refle cts combined immune, endocrine, and receptor-level alterations rather than a purely local endometrial effect. Overall, these findings support an integrated immuno-endocrine model in which chronic inflammation (elevated IL -6) and hyperprolactinemia are associated with reduced Progesterone Receptor (PGR) gene expression and altered progesterone signaling, contributing to an unfavorable implantation environment (15). The stronger negative correlation between IL -6 and PGR expression suggests a closer link with inflammatory pathways, while prolactin shows an independent association with case status. The present study highlights the clinical relevance of concurrent inflammatory and hormonal alterations in women with Recurrent Pregnancy Loss (RPL) and endometriosis. Elevated IL -6 and prolactin levels associated with reduced Progesterone Receptor (PGR) expression suggest involvement of integrated immuno- endocrine pathways. These findings may guide future research into targeted strategies addressing inflammatory and hormonal dysregulation. However, as an observational design, the study demonstrates associations rather than causality or mechanisms. Accordingly, potential clinical applications including dopamine agonists, anti -inflammatory, or immunomodulatory therapies should be interpreted with caution. Further validation through prospective and inte rventional studies is required before clinical implementation. A key strength of this study is the integrated evaluation of Inflammatory (IL-6), endocrine (prolactin), and molecular (PGR gene expression) factors within a well -defined case -control cohort of women with RPL and endometriosis. Unlike prior studies that assessed these pathways separately, this approach enables analysis of their interactions and combined influence on progesterone receptor regulation. Quantification of PGR mRNA by qRT- PCR provides objective molecular evidence beyond clinical and hormonal measures. Firth penalized logistic regression improved estimate stability in the presence of strong group separation, while ROC analysis demonstrated the discriminatory potential of circulating PGR expression. Overall, the multi - parameter approach and advanced statistical methods enhance methodological rigor and internal validity. Despite these strengths, several limitations should be considered. The case -control design precludes causal inference. PGR expression was measured in peripheral blood rather than endometrial tissue, reflecting systemic progesterone responsiveness and limiting

about local receptor activity or PR -A/PR- B balance. Future s tudies incorporating paired endometrial analysis are needed to clarify systemic - local relationships. Although sufficient for detecting group differences, the sample size may limit generalizability and reduce power in multivariable analyses, as reflected by attenuation after adjustment. The absence of functional assays further limits mechanistic interpretation of the results of the present study. 5. Conclusion In conclusion, chronic inflammation (elevated IL -6) and hyperprolactinemia are associated with reduced Bobby Joseph, et al. 828 Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research Progesterone Receptor (PGR) gene expression in women with RPL and endometriosis, potentially impairing progesterone signaling and contributing to implantation failure. The stronger association between IL-6 and PGR expression suggests a closer link with inflammatory pathways, while prolactin independently increases disease risk. These findings highlight systemic alterations in PGR expression and support an integrated immuno-endocrine model beyond localized endometrial mechanisms. 6. Declarations Acknowledgments The authors would like to sincerely appreciate the support and resources provided by Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, India, and Genetika, Centre for Advanced Genetic Studies, Thiruvananthapuram, Kerala, India. Ethical Considerations The study protocol was approved by the Institutional Ethics Committee of Genetika (Reg. No. EC/NEW/INST/2025/KL/0661; Approval No: 19/2023/IECG), and all procedures adhered to the ethical standards outlined in the Declaration of Helsinki (1964 and its later amendments). Authors' Contributions B.J was responsible for conceptualization, data curation, investigation, methodology, and original draft writing. B.K, A.S, D.R.D provided supervision, project administration, and resources, guiding the overall direction and execution of the study. A.S.D.R .D, A.R and J.J.S.N. contributed to formal analysis and validation. F.O and S.U supported data curation and investigation. Review and editing were overseen by A.S and D.R.D. All the authors read and approved the final manuscript. Conflict of Interest No potential conflict of interest was reported by the authors. Fund or Financial Support This research received no external funding. 1. Eshre Guideline Group on RPL, Bender Atik R, Christiansen OB, Elson J, Kolte AM, Lewis S, et al. ESHRE guideline: recurrent pregnancy loss: an update in 2022. Hum Reprod Open. 2023;2023(1):hoad002. [doi:10.1093/hropen/hoad002] 2. Sudharma D, Muninathan N, Suresh A, Parthasarathy M, Krishnakumary AR, Mullukalayil Joseph S, et al. Synergistic Effects of IL -16 and KRAS in Endometriosis with Emphasis on Oxidative Stress. J Obstet Gynecol Cancer Res. 2026;11(4):358 -66. [doi:10.24200/jogcr.11.4.358] 3. Laijawala RA. Recurrent Pregnancy Loss: Immunological aetiologies and associations with mental health. Brain Behav Immun Health. 2024;41:100868. [doi:10.1016/j.bbih.2024.100868] 4. Garmendia JV, De Sanctis CV, Hajdúch M, De Sanctis JB. Endometriosis: an immunologist’s perspective. Int J Mol Sci. 2025;26(11):5193. [doi:10.3390/ijms26115193] 5. Vaisbuch E, Erez O, Romero R. Physiology of progesterone. In: Progestogens in Obstetrics and Gynecology. Cham: Springer International Publishing; 2021:1 -29. [ doi:10.1007/978-3- 030-52508-8_1] 6. Gomaa IA, Sabry A, Allam IS, Ashoush S, Reda A. Endometrial progesterone and estrogen receptors in relation to hormonal levels in women with unexplained recurrent miscarriage. Rev Bras Ginecol Obstet. 2023;45(11):e676-82. [doi:10.1055/s-0043-1776030] 7. Ye Z, Meng Q, Zhang W, He J, Zhao H, Yu C, et al. Exploration of the shared gene and molecular mechanisms between endometriosis and recurrent pregnancy loss. Front Vet Sci. 2022;9:867405. [doi:10.3389/fvets.2022.867405] 8. Incognito GG, Di Guardo F, Gulino FA, Genovese F, Benvenuto D, Lello C, et al. Interleukin-6 as a useful predictor of endometriosis-associated infertility: a systematic review. Int J Fertil Steril. 2023;17(4):226-30. [doi:10.22074/ijfs.2023.557683.1329] 9. Moustakli E, Potiris A, Zikopoulos A, Drakaki E, Arkoulis I, Skentou C, et al. Immunological

829 Prolactin and PGR Expression in Endometriosis RPL Volume 11, September 2026 Journal of Obstetrics, Gynecology and Cancer Research factors in recurrent pregnancy loss: mechanisms, controversies, and emerging therapies. Biology (Basel). 2025;14(7):877. [doi:10.3390/biology14070877] 10. Presicce P, Roland C, Senthamaraikannan P, Cappelletti M, Hammons M, Miller LA, et al. IL-1 and TNF mediates IL -6 signaling at the maternal-fetal interface during intrauterine inflammation. Front Immunol. 2024;15:1416162. [doi:10.3389/fimmu.2024.1416162] 11. Qi Q, Li Y, Chen Z, Luo Z, Zhou T, Zhou J, et al. Update on the pathogenesis of endometriosis- related infertility based on contemporary evidence. Front Endocrinol (Lausanne). 2025;16:1558271. [doi:10.3389/fendo.2025.1558271] 12. Bonavina G, Taylor HS. Endometriosis - associated infertility: From pathophysiology to tailored treatment. Front Endocrinol (Lausanne). 2022;13:1020827. [doi:10.3389/fendo.2022.1020827] 13. Li L, Baek KH. Exploring potential biomarkers in recurrent pregnancy loss: A literature review of omics studies to molecular mechanisms. Int J Mol Sci. 2025;26(5):2263. [doi:10.3390/ijms26052263] 14. Huang S, Mu F, Wang K, Wang F. Key protein identification in endometrium of recurrent pregnancy loss patients through integrated proteomic and transcriptomic analysis. J Matern Fetal Neonatal Med. 2026;39(1):2651458. [doi:10.1080/14767058.2026.2651458] 15. Abuhassan Q, Oriquat G, Menon SV, Chanania K, Inbathamizh L, Mukherjee G, et al. The molecular puzzle of recurrent implantation failure: integrating genetic, epigenetic, and immune mechanisms for precision reproductive medicine. J Assist Reprod Genet. 2026;43(5):1349-63. [doi:10.1007/s10815-026- 03832-2] How to Cite This Article: Joseph B, Kanniappan B, Roy Divakaran D, Suresh A, Sundaresh A, Rajan A, et al . Systemic Inflammation and Hyperprolactinemia as Determinants of Progesterone Receptor Gene Expression in Recurrent Pregnancy Loss with Endometriosis. J Obstet Gynecol Cancer Res. 2026;11(9):820-829. Download citation: RIS | EndNote | Mendeley |BibTeX |