{"paper_id":"318b48e4-fd14-4973-b2fa-47042d101420","body_text":"Orginal Article  | JOGCR. 2026; 11(9): 820-829 \n     Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \n Journal of Obstetrics, Gynecology and Cancer Research | ISSN: 2476-5848 \n \n \nSystemic Inflammation and Hyperprolactinemia as Determinants of \nProgesterone Receptor Gene Expression in Recurrent Pregnancy Loss with \nEndometriosis \n \nBobby Joseph1 , Boopathi Kanniappan2* , Dinesh Roy Divakaran3* , Arumugam Suresh4 , Aswathy \nSundaresh5 , Aswathi Rajan6 , Jiju Jayasree Sasidharan Nair7 , Fawas Olpamkadavath1, Sigha \nUnnikrishnan1  \n \n1. Research Scholar, Meenakshi Academy of Higher Education and Research (Deemed to be University), Chennai, Tamil \nNadu, India \n2. Department of Plastic Surgery, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of \nHigher Education and Research (Deemed to be University) Kanchipuram, Tamil Nadu, India \n3. CEO & Senior Cytogeneticist Genetika, Centre for Advanced Genetic Studies, Thiruvananthapuram, Kerala, India \n4. Central Research Laboratory, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of \nHigher Education and Research (Deemed to be University) Chennai, Tamil Nadu, India \n5. Sree Gokulam Medical College & Research Foundation, Venjaramoodu, Thiruvananthapuram, Kerala, India \n6. Department of Biochemistry, Sree Uthradom Thirunal Academy of Medical Sciences (SUTAMS), Vattappara, \nThiruvananthapuram, Kerala, India \n7. Department of Pathology, F.I.S.H Laboratory, Regional Cancer, Medical College, Thiruvananthapuram, Kerala, India  \nArticle Info  ABSTRACT \n  \n 10.24200/jogcr.11.9.820 \n \n \n \nBackground & Objective:  Recurrent Pregnancy Loss (RPL) in women with \nendometriosis involves immune and hormonal dysregulation affecting Progesterone \nReceptor (PGR) signaling. Systemic inflammation and elevated prolactin may impair \nthis pathway and contribute to pregnancy failure. This study was conducted with aim to \nevaluate the association of serum Interleukin-6 (IL -6), prolactin, and Progesterone \nReceptor (PGR) gene expression, and their diagnostic potential. \nMaterials & Methods: This case-control study was conducted on 250 women (125 with \nRPL and endometriosis; 125 controls). Serum IL -6 and prolactin were measured by \nELISA, and PGR mRNA expression was quantified using real -time PCR. Group \ncomparisons were performed using non -parametric test s. Correlation, logistic \nregression, and ROC analyses were applied. P<0.05 was considered statistically \nsignificant. \nResults: IL-6 and prolactin levels were significantly higher, while PGR expression was \nsignificantly lower in cases compared to controls (P<0.001). PGR expression showed \nthe highest diagnostic performance, followed by prolactin and IL-6. IL-6 and prolactin \nwere negatively associated with PGR expression, and prolactin remained an \nindependent predictor after adjustment. \nConclusion: These findings demonstrate that inflammatory and hormonal alterations \nare associated with reduced progesterone receptor expression, indicating their potential \nrole in RPL among women with endometriosis. \nKeywords: Recurrent Pregnancy loss, Endometriosis, Progesterone receptor gene \nexpression, Interleukin -6, Prolactin \n \nReceived: 2026/02/17 \nAccepted: 2026/04/28 \nPublished Online: 18 Jul. 2026 \n \n \n \n \n \nCorresponding Information:  \nBoopathi Kanniappan, \nDepartment of Plastic Surgery, Meenakshi \nMedical College Hospital & Research Institute, \nMeenakshi Academy of Higher Education and \nResearch (Deemed to be University)  \nKanchipuram, Tamil Nadu, India \n \nEmail: drkbpathi@gmail.com \n \nDinesh Roy Divakaran, \nCEO & Senior Cytogeneticist Genetika, Centre \nfor Advanced Genetic Studies, \nThiruvananthapuram, Kerala, India \n \nEmail: drdineshroyd@gmail.com \n \n \nCopyright © 2026, This is an original open-access article distributed under the terms of the Creative Commons Attribution-noncommercial \n4.0 International License which permits copy and redistribution of the material just in noncommercial usages with proper citation . \n \n \n1. Introduction\nRecurrent Pregnancy Loss (RPL), defined by \nESHRE (2023) as two or more losses before 24 weeks \nof gestation, affects approximately 2 -5% of women of \nreproductive age and remains a multifactorial clinical \nchallenge involving genetic, hormonal, anatomical, \nimmunological, and inflammatory factors (1). Despite \nthorough evaluation, many RPL cases remain \nunexplained, suggesting a role for endocrine -immune \ninteractions. Endometriosis, an estrogen -dependent \ninflammatory condition, is increasingly recognized in \nthese women and may impair implantation, disrupt \nendometrial receptivity, and contribute to early \n\n\n821 Prolactin and PGR Expression in Endometriosis RPL \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \npregnancy loss (2,3). RPL with endometriosis \nrepresents a complex subgroup where hormonal \ndysregulation and chronic inflammation interact to \nworsen reproductive dysfunction. \nProgesterone is essential for pregnancy \nestablishment and maintenance, acting via the \nProgesterone Receptor (PGR) gene, which encodes a \nligand-activated transcription factor on chromosome \n11q22.1 (3). It regulates endometrial receptivity, \ndecidualization, trophoblast -maternal communication, \nand immune tolerance at the maternal -fetal interface \nthrough genomic signaling (4). Endometrial \nresponsiveness depends on the PR -A/PR-B isoform \nbalance, with dysregulation linked to progesterone \nresistance in endometriosis and RPL (5,6). \nProgesterone receptors are also expressed in immune \ncells, where they regulate cytokine production and \ndifferentiation; thus, peripheral PGR expression may \nreflect systemic progesterone -mediated immune \nsignaling rather than local endometrial receptor \ndensity. \nIn women with coexisting RPL and endometriosis, \naltered PGR expression may reflect systemic immuno-\nendocrine dysregulation driven by interacting \ninflammatory and hormonal pathways. Reduced \nperipheral PGR may indicate impaired progesterone -\nmediated immunore gulation and an adverse \ninflammatory reproductive phenotype. Chronic \ninflammation, particularly Interleukin-6 (IL-6), plays a \ncentral role in endometriosis and reproductive failure \nby promoting aberrant immune responses, lesion \nproliferation, angiogenesis, immune recruitment, and \nprogesterone resistance (7). Although IL-6 findings in \nRPL are inconsistent (8), persistent IL -6 driven \ninflammation in coexisting RPL and endometriosis \nmay impair immune tolerance, creating a pro -\ninflammatory, less receptive, and progesterone -\nresistant uterine environment that increases pregnancy \nloss risk. \nHyperprolactinemia, a common endocrine \nabnormality, may impair endometrial receptivity by \ndisrupting the hypothalamic -pituitary-gonadal axis, \nreducing luteal function and progesterone synthesis \n(9). Prolactin also exerts immunomodulatory effects, \ninfluencing T -cell activation and cytokine release, \npotentially exacerbating endometrial inflammation. \nEmerging evidence suggests it may alter PGR gene \nexpression, compounding inflammatory effects in \nwomen with endometriosis and RPL. \nAlthough inflammation, hyperprolactinemia, and \nprogesterone resistance are individually linked to \nreproductive failure, their combined effect on PGR \ngene expression in women with RPL and \nendometriosis remains unclear. This study was \nconducted with aim to e xamines the association of \ninflammatory and prolactin alterations with PGR \nexpression, aiming to clarify underlying immuno -\nendocrine mechanisms and identify potential \ndiagnostic markers. \n \n2. Materials and Methods \nThis case -control study was conducted on 250 \nwomen aged 25 -45 years (125 with RPL and \nendometriosis; 125 healthy controls) in 2023 -2025. \nWritten informed consent was obtained, and \ndemographic, lifestyle, and clinical data were collected \nusing a structured questionnaire. Clinical evaluation, \nsample collection, and laboratory analyses were \nconducted under standardized conditions in \ncollaboration with multiple centers. Controls were \nrecruited from community -based health screening \nprograms. \nInclusion criteria for case group were women aged \n25-45 years with clinically confirmed Recurrent \nPregnancy Loss (RPL), defined as two or more \nconsecutive losses before 24 weeks of gestation \n(ESHRE guidelines), and coexisting endometriosis. \nEndometriosis was diagnosed using standard criteria, \nincluding transvaginal ultrasonography with \nconfirmation by laparoscopy and /or histopathology \nwhere available. Consistent inclusion criteria were \napplied to minimize clinical heterogeneity. \nAge-matched fertile women aged 25 -45 years with \nno history of recurrent pregnancy loss or evidence of \nendometriosis were included as controls. Fertility was \nconfirmed by at least one prior full-term pregnancy. All \ncontrols had regular menstrual cycles (21-35 days) and \nmet the same exclusion criteria as cases. \nParticipants were excluded if they had autoimmune \nor unrelated endocrine disorders, uterine structural \nabnormalities, active pelvic infections, malignancy, or \nchronic systemic diseases. Those who had received \nhormonal or immunomodulatory therapy within the  \nprevious three months or had incomplete clinical data \nwere also excluded. \nSample size was calculated using the formula \nZ²pq/d², where Z is the standard normal deviation, p the \nestimated prevalence from published data, q=1−p, and \nd the margin of error. The prevalence was derived from \nexisting literature to ensure an adequate sample size. \nAll participants provided information on their \ndemographics, medical history, and lifestyle through a \nstructured questionnaire. Their height, weight, and \nwaist circumference were measured using standardized \nprocedures. \nFasting venous blood (8-10 mL) was collected under \naseptic conditions into plain tubes for inflammatory \nand hormonal assays and EDTA tubes for molecular \nanalysis. Samples were processed within 60 minutes. \nPlain tubes were allowed to clot (15 -30 minutes, room \ntemperature) and centrifuged at 3000 rpm for 10 \nminutes to obtain serum, which was stored at -20°C \nuntil analysis. RNA extraction was performed from \nEDTA samples under RNase-free conditions. \nSerum Interleukin-6 (IL -6) levels were quantified \nusing a commercial sandwich ELISA kit (Cat. No. \n\nBobby Joseph, et al. 822 \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \nOPK1156; Origin Diagnostics and Research) \naccording to the manufacturer’s protocol. Standards \nand serum samples were added to pre -coated wells, \nfollowed by incubation with biotinylated detection \nantibody and streptavidin -HRP. After washing, TMB \nsubstrate was added, and the reaction was stopped. \nAbsorbance was measured at 450 nm, and \nconcentrations were calculated from a standard curve. \nResults were expressed in pg/mL. \nSerum Prolactin levels were measured using a \ncommercially available sandwich ELISA kit (Category \nNo. OPK1224) manufactured by Origin Diagnostics \nand Research. The assay was performed according to \nthe manufacturer’s protocol. Absorbance was read at \n450 nm u sing a microplate reader, and prolactin \nconcentrations were determined from the standard \ncalibration curve. Results were expressed in ng/mL. \nTo minimize physiological variation in prolactin \nlevels, blood samples were collected under \nstandardized conditions, preferably in the morning \nhours following an overnight fast. Efforts were made \nto ensure consistency in the timing of sample collection \nacross participants. Participants with known endocrine \ndisorders, including thyroid dysfunction and pituitary \nabnormalities, were excluded to reduce potential \nconfounding effects on prolactin levels.  \nMolecular Analysis \nRNA Isolation and cDNA synthesis. \nTotal RNA was extracted using an RNA isolation kit \n(Origin Diagnostics and Research; Cat. No. OPD419) \nbased on guanidine thiocyanate -phenol-chloroform \nextraction with spin -column purification. Samples \nwere homogenized in Buffer RZ to ensure complete \nlysis, and for blood samples, three volumes of Buffer \nRZ were added and mixed thoroughly. \nFollowing homogenization, chloroform (200 µL per \n1 mL Buffer RZ) was added, mixed vigorously, and \nincubated for 3 minutes at room temperature. Samples \nwere centrifuged at 12,000 rpm for 10 minutes at 4°C \nto separate phases. The aqueous phase was transferre d \nto a new tube, mixed with an equal volume of ethanol \n(50–100%), and loaded onto an RNase -free spin \ncolumn for RNA binding. The column was washed \nwith Buffer RW and Buffer RZ, followed by \ncentrifugation to remove residual ethanol. RNA was \neluted in 30 µL RNase-free water by centrifugation at \n12,000 rpm for 2 minutes. \nRNA purity was assessed using an Eppendorf \nBioSpectrometer based on the A260/A280 ratio, with \nvalues of 1.8 -2.0 considered acceptable. To minimize \ndegradation, RNA was immediately reverse -\ntranscribed into cDNA under RNase-free conditions. \nSamples meeting quality criteria were subsequently \nreverse-transcribed into Complementary DNA (cDNA) \nusing a cDNA synthesis kit from the same \nmanufacturer (Cat. No. ODR41), employing a blend of \noligo (dT)₁₈ and random hexamer primers with reverse \ntranscriptase, under the manufacturer’s recommended \nreaction conditions. The synthesized cDNA was \naliquoted and stored at -20°C until downstream gene \nexpression analysis \nQuantitative Real-Time PCR (qRT-PCR) \nPGR gene expression was quantified using SYBR \nGreen-based qRT -PCR with specific primers \n(Forward: 5′ -GTCGCCTTAGAAAGTGCTGTCAG-\n3′; Reverse: 5′-GCTTGGCTTTCATTTGGAACGCC-\n3′). GAPDH was used as the reference gene due to its \nstable expression in peripheral blood leukocytes and \nvalidated use in gene expression studies. \nGene expression was normalized to GAPDH to \ncontrol for variation in RNA input and cDNA \nsynthesis. Relative expression was calculated using the \ncomparative Ct (ΔΔCt) method, where ΔCt=Ct(PGR) - \nCt(GAPDH), and ΔΔCt  was derived relative to the \ncontrol group. Fold change was expressed as 2^−ΔΔCt. \nRNA extraction, cDNA synthesis, and PCR setup were \nperformed in separate designated areas to prevent \ncontamination. All steps were conducted in accordance \nwith Good Laboratory Practice (GLP) guidelines. \nPGR primers (23 bp) were procured from Eurofins \nGenomics India Pvt. Ltd. and validated in silico using \nNCBI Primer-BLAST to ensure specific amplification. \nqRT-PCR was performed in a 20 μL  reaction \ncontaining SYBR Green 2X Master Mix, primers, \ncDNA, and nuclease -free water. Thermal cycling \nincluded initial denaturation at 95°C for 5 minutes, \nfollowed by 30-40 cycles of 94°C (1 minute), 60°C (1 \nminute), and 72°C (1 minute), with a final extension at \n72°C for 10 minutes and melt curve analysis. Gene \nexpression was normalized to a housekeeping gene and \ncalculated using the comparative Ct method. \nData were analyzed using Stata 17.0. Normality of \ndata was assessed using the Shapiro -Wilk test. Non -\nnormally distributed variables were expressed as \nmedian (IQR) and compared using the Mann -Whitney \nU test. Associations between IL -6, prolactin, PGR \nexpression, and case status were evaluated using binary \nlogistic regression. Correlations were assessed using \nSpearman’s rank test. ROC curve analysis was \nperformed to evaluate discriminatory abilit y, with \nAUC (95% CI) calculated and optimal cut -off values \ndetermined using the Youden Index. P<0.05 was \nconsidered statistically significant. \n \n3. Results \nA total of 250 women were enrolled in this study. \nAmong the study population, 125 cases were clinically \ndiagnosed with Recurrent Pregnancy Loss (RPL) with \ncoexisting endometriosis, and 125 age -matched \nwomen without a history of RPL or endometriosis \nserved as controls. \nTable 1 shows that IL-6 and prolactin levels were not \nnormally distributed in either cases or controls (all \nP<0.05). PGR gene expression was also non -normally \n\n823 Prolactin and PGR Expression in Endometriosis RPL \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \ndistributed in cases ( P<0.01), but  followed an \napproximately normal distribution in controls \n(P=0.077). Overall, most variables violated the \nassumption of normality. \n \n \nData represent P-values from the Shapiro-Wilk test. Statistical significance was set at P<0.05 \n \nTable 2  shows that cases had significantly higher \nserum IL-6 and prolactin levels compared to controls \n(P<0.01), indicating increased inflammatory activity \nand hormonal dysregulation. In contrast, progesterone \nreceptor gene expression was significantly reduced in \ncases ( P<0.01), suggesting impaired progesterone \nsignaling in cases. \nReceiver Operating Characteristic (ROC) analysis \ndemonstrated good diagnostic performance for all three \nmarkers. The PGR gene showed the highest \ndiscriminative ability (AUC=0.870), followed by \nProlactin (AUC=0.846) and IL -6 (AUC=0.838), \nindicating strong accuracy in distinguishing cases from \ncontrols. All AUC values were statistically significant \n(P<0.01). At the optimal cut -off values (Prolactin \n≥18.4, IL -6≥6.2 pg /mL, PGR≤0.80), PGR achieved \n100% specificity and positive predictive value, \nmeaning it perfectly identified non-cases and produced \nno false positives in this sample (LR+ =∞). IL -6 \ndemonstrated the highest sensitivity –specificity \nbalance after PGR, with strong specificity (88%) and a \nhigh positive likelihood ratio (6.0). Prolactin also \nshowed good overall diagnostic accuracy with \nbalanced sensitivity (76%) and specificity (80%). \nOverall, PGR appears to be the strongest diagnostic \nmarker among the three, wit h IL-6 and prolactin also \nshowing clinically meaningful discriminative \nperformance. \nFigure 1  and 2 summarizes the diagnostic \nperformance of inflammatory, hormonal parameters \nand receptor gene biomarkers in differentiating cases \nfrom controls using receiver operating characteristic \nanalysis. IL -6 exhibits good diagnostic accuracy \n(AUC=0.838) (Table 3, Fig 1), as reflected by its ROC \ncurve lying well above the reference line. This \nindicates meaningful sensitivity and specificity in \ndistinguishing cases from controls. The result \nreinforces the role of systemic inflammation as a \nsignificant contributor to disea se pathophysiology. \nProgesterone receptor gene expression demonstrates \nstrong discriminatory performance (AUC=0.870) \n(Table 3, Fig 2), with its ROC curve positioned close to \nthe upper -left corner. This indicates high sensitivity \nand specificity in differentiating cases from controls. \nThe finding suggests that reduced PGR gene \nexpression is a robust molecular marker and supports \nthe concept of impaired progesterone signaling or \nprogesterone resistance in the affected group. Prolactin \nshows strong discriminatory capability (AUC=0.846) \n(Table 3, Fig 2), with a ROC curve approaching the \nupper-left region. This suggests that elevated prolactin \nlevels effectively differentiate affected women from \ncontrols, highlighting altered endocrine regulation as \nan important component of the underlying condition. \nThe correlation coefficient ( P= −0.3729) shown in \nfigure 3  indicates a weak negative and statistically \nsignificant association between prolactin concentration \nand progesterone receptor gene expression. The points \nshow a gentle downward tendency rather than a steep \nfall. Higher prolactin values are more frequently  \naccompanied by lower receptor expression, but the \nscatter remains relatively broad, reflecting the weaker \nmagnitude of the association. \nInflammation demonstrates a more pronounced \neffect. The association between IL -6 and progesterone \nreceptor gene expression is moderate -to-strong in the \nnegative direction and statistically significant ( P= \n−0.5437). Figure 4  thus indicates that increasing \ninflammatory burden may substantially suppress \nprogesterone receptor gene activity. This finding points \ntoward a possible inflammatory mechanism that could \ninterfere with hormonal signaling and contribute to \ncase status. \nFirth penalized logistic regression ( Table 4) showed \na strong inverse association between peripheral PGR \nexpression and case status. In the unadjusted model, \nreduced PGR expression demonstrated near -complete \nseparation (OR=0.0003, P<0.01). After adjustment for \nIL-6 and prolactin, the association attenuated and \nbecame borderline significant (OR=0.004, P=0.057), \nindicating partial influence of inflammatory and \nendocrine factors. Prolactin remained an independent \npredictor (OR=1.110, P=0.005), whereas IL-6 was not \nsignificant. These findings suggest that \nhyperprolactinemia independently contributes to \nTable 1. Shapiro-Wilk test for normality of biochemical, hormonal and gene expression parameters among cases and \ncontrols \n Shapiro–Wilk p-value \nVariable Cases Controls \nIL-6 (pg/mL) <0.01 <0.01 \nProlactin (ng/mL) 0.002 <0.01 \nProgesterone receptor gene expression <0.01 0.077 \n\nBobby Joseph, et al. 824 \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \ndisease risk, while reduced PGR expression reflects \nsystemic receptor dysregulation influenced by the \ninflammatory-hormonal milieu. \n \nTable 2. Comparison of inflammatory, hormonal and gene expression parameters between cases and controls \nParameter \nCases (n = 125) Median \n(IQR) \nControls (n =125) \nMedian (IQR) \nP-value \nIL-6 (pg/mL) 9.5(5.3-15.0) 3.2(1.4-4.7) <0.001* \nProlactin (ng/mL) 28.4(22.1-35.6) 14.7(10.3-19.8) <0.001* \nPGR expression (2-ΔΔCt) 0.6(0.3-0.9) 1.0(1.0-1.1) <0.001* \nData are presented as median (interquartile range, IQR). Between-group comparisons were performed using the Mann-Whitney U test due to \nnon-normal distribution (Shapiro-Wilk P<0.05). A two-tailed P-value<0.05 was considered statistically significant. \n \n \n \nAUC=Area Under the Receiver Operating Characteristic Curve; CI=Confidence Interval; LR+ =Positive Likelihood Ratio; LR – =Negative \nLikelihood Ratio; PPV=Positive Predictive Value; NPV=Negative Predictive Value. Optimal cut -off values were determined using R OC curve \nanalysis. An AUC>0.5 indicates discriminatory ability. LR+ =Sensitivity/(1 -Specificity); LR – =(1-Sensitivity)/ Specificity. An infinite LR+ \nindicates perfect specificity (no false positives) in the study sample \n \nTable 3. Diagnostic performance of hormonal and receptor biomarkers in distinguishing cases from controls \nMetric Prolactin IL-6 (pg/mL) PGR gene \nArea under curve (AUC) 0.846 0.838 0.870 \n95% CI 0.797-0.894 0.788-0.889 0.825-0.915 \nP-value (AUC > 0.5) <0.01 <0.01 <0.01 \nOptimal cut-off ≥18.4 ≥6.2 ≤0.80 \nSensitivity (%) 76.0 72.0 70.0 \nSpecificity (%) 80.0 88.0 100.0 \nPositive likelihood ratio (LR+) 3.8 6.0 ∞ \nNegative likelihood ratio (LR–) 0.30 0.32 0.30 \nPositive predictive value (%) 79.2 85.7 100.0 \nNegative predictive value (%) 76.9 75.9 77.2 \n\n825 Prolactin and PGR Expression in Endometriosis RPL \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \nTable 4.  Firth penalized logistic regression showing association between progesterone receptor gene expression and \ncase-control status \nPredictor Model 1 \nOR \n95% CI P-value \nModel 2 \nOR \n95% CI P-value \nProgesterone receptor gene \nexpression 0.0003 0.000-0.003 <0.01 0.004 0.000-1.181 0.057 \nIL-6    1.228 0.893-1.689 0.207 \nProlactin    1.110 1.031-1.194 0.005 \n \n \nFigure 1. Receiver operating characteristic (ROC) curves showing the diagnostic performance of inflammatory marker \n(IL-6) \nROC curves showing the diagnostic performance of Interleukin-6 (IL-6), Fasting Blood Sugar (FBS), serum total cholesterol, triglycerides, Low-\nDensity Lipoprotein (LDL), and High-Density Lipoprotein (HDL) in distinguishing cases from controls. Sensitivity is plotted against 1- specificity. \nThe reference diagonal line represents no discriminatory ability (AUC=0.5). Higher curve deviation toward the upper left corn er indicates better \ndiagnostic performance. (n=125) \n \n \n \nFigure 2 . Receiver Operating Characteristic (ROC) curves showing the diagnostic performance of hormonal, and \nprogesterone receptor gene expression markers (Prolactin and PGR Gene Expression) \nROC curves illustrating the diagnostic performance of Thyroid hormones (T3, T4, TSH), Luteinizing Hormone (LH), Follicle-Stimulating \nHormone (FSH), prolactin, estrogen, progesterone, and Progesterone Receptor (PGR) gene expression in distinguishing cases from controls. \nSensitivity is plotted against 1- specificity. The reference diagonal line represents no discriminatory ability (AUC=0.5), with curves closer to the \nupper left corner indicating better diagnostic performance. (n=125) \n\n\nBobby Joseph, et al. 826 \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \n \n \nFigure 3. Relationship between serum prolactin levels and progesterone receptor gene expression among cases \nScatter plot illustrates the relationship between serum prolactin levels (ng/mL) and Progesterone Receptor (PGR) gene expression. A negative \ncorrelation was observed (Spearman’s P= −0.3729, P<0.01), indicating that higher prolactin levels are associated with reduced PGR expression. \nThe red line represents the fitted regression trend (n=125). \n \n \nFigure 4. Relationship between serum Interleukin-6 (IL-6) levels and progesterone receptor gene expression among cases \nScatter plot showing the relationship between serum Interleukin-6 (IL-6) levels (pg/mL) and Progesterone Receptor (PGR) gene expression. A \nsignificant negative correlation was observed (Spearman’s P= −0.5437, P<0.01), indicating that higher IL -6 levels are associated with reduced \nPGR expression. The red line represents the fitted regression trend (n=125). \n \n4. Discussion \nTo the best of our knowledge, this is the first study \nwhich evaluated the combined effects of IL-6 mediated \ninflammation and hyperprolactinemia on PGR gene \nexpression (mRNA, qRT -PCR) in a case -control \ncohort of women with RPL and endometriosis. \nPrevious studies have examined progesterone \nresistance, cytokines, or prolactin separately, often in \nRPL or endometriosis alone. By integrating these \nfactors in a dual -pathology subgroup, this study \naddresses a key gap and highlights interactions \nunderlying impaired progesterone signaling. \nSerum IL-6 levels were significantly higher in cases \nthan controls, indicating increased systemic \ninflammation. This aligns with evidence linking IL -6 \nto immune signaling at the maternal-fetal interface (10) \nand inflammatory processes in endometriosis -related \ninfertility (11). While findings in RPL alone are \n\n\n827 Prolactin and PGR Expression in Endometriosis RPL \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \ninconsistent (12), the coexistence of RPL and \nendometriosis may represent a distinct inflammation -\nassociated phenotype. Although IL -6 showed good \ndiscriminatory performance (AUC=0.846), it was not \nindependently significant in adjusted Firth logistic \nregression, suggesting overlap with other factors. \nUnlike prior studies assessing IL -6 in isolation, this \nstudy evaluates inflammatory burden alongside PGR \ngene expression within a multivariable framework, \nproviding insight into interactions between \ninflammatory, endocrine, and molecular parameters in \nthis combined phenotype (13). \nProlactin levels were significantly higher in cases \nthan controls, indicating hyperprolactinemia. Elevated \nprolactin is associated with reproductive dysfunction \nthrough disruption of the hypothalamic -pituitary-\novarian axis, impaired luteal function, and reduced \nprogesterone production, consistent with previous \nreports (11). In this study, prolactin showed strong \ndiscriminatory performance on ROC analysis and a \nweak but significant negative correlation with PGR \nexpression. Importantly, it remained independently \nassociated with case status in adjusted Firth logistic \nregression, suggesting an independent role rather than \na secondary effect. In the context of systemic \ninflammation, elevated prolactin may be linked to \naltered progesterone signaling, reflecting coordinated \nendocrine–immune interactions. This integrated \nperspective extends prior research that has largely \nevaluated hyperprolactinemia in isolation. \nProgesterone Receptor (PGR) gene expression was \nsignificantly lower in our cases than controls, \nindicating altered progesterone signaling at a systemic \nlevel in women with RPL and endometriosis. Notably, \nprogesterone receptor isoforms PR -A and PR -B have \ndistinct roles, with PR -B mediating transcriptional \nactivation and PR -A modulating PR -B activity; \nimbalance between these isoforms has been implicated \nin progesterone resistance in endometriosis and RPL. \nWhile prior studies have reported reduced receptor \nexpression in endometrios is and altered receptor \ndynamics in RPL (14), these primarily focused on \nendometrial tissue. In contrast, this study assessed PGR \nmRNA in peripheral blood, providing insight into \nsystemic regulation. PGR expression showed strong \ndiscriminatory ability in ROC analysis. In unadjusted \nFirth logistic regression, it was strongly associated with \ncase status, but this association attenuated after \nadjustment, suggesting influence from upstream \ninflammatory and hormonal factors. These findings \nsupport an integrated model in which progesterone \nresistance refle cts combined immune, endocrine, and \nreceptor-level alterations rather than a purely local \nendometrial effect. \nOverall, these findings support an integrated \nimmuno-endocrine model in which chronic \ninflammation (elevated IL -6) and hyperprolactinemia \nare associated with reduced Progesterone Receptor \n(PGR) gene expression and altered progesterone \nsignaling, contributing to an unfavorable implantation \nenvironment (15). The stronger negative correlation \nbetween IL -6 and PGR expression suggests a closer \nlink with inflammatory pathways, while prolactin \nshows an independent association with case status. \nThe present study highlights the clinical relevance of \nconcurrent inflammatory and hormonal alterations in \nwomen with Recurrent Pregnancy Loss (RPL) and \nendometriosis. Elevated IL -6 and prolactin levels \nassociated with reduced Progesterone Receptor (PGR) \nexpression suggest involvement of integrated immuno-\nendocrine pathways. These findings may guide future \nresearch into targeted strategies addressing \ninflammatory and hormonal dysregulation. However, \nas an observational design, the study demonstrates \nassociations rather than causality or mechanisms. \nAccordingly, potential clinical applications including \ndopamine agonists, anti -inflammatory, or \nimmunomodulatory therapies should be interpreted \nwith caution. Further validation through prospective \nand inte rventional studies is required before clinical \nimplementation. \nA key strength of this study is the integrated \nevaluation of Inflammatory (IL-6), endocrine \n(prolactin), and molecular (PGR gene expression) \nfactors within a well -defined case -control cohort of \nwomen with RPL and endometriosis. Unlike prior \nstudies that assessed these pathways separately, this \napproach enables analysis of their interactions and \ncombined influence on progesterone receptor \nregulation. Quantification of PGR mRNA by  qRT-\nPCR provides objective molecular evidence beyond \nclinical and hormonal measures. Firth penalized \nlogistic regression improved estimate stability in the \npresence of strong group separation, while ROC \nanalysis demonstrated the discriminatory potential of \ncirculating PGR expression. Overall, the multi -\nparameter approach and advanced statistical methods \nenhance methodological rigor and internal validity. \nDespite these strengths, several limitations should be \nconsidered. The case -control design precludes causal \ninference. PGR expression was measured in peripheral \nblood rather than endometrial tissue, reflecting \nsystemic progesterone responsiveness and limiting \nconclusions about local receptor activity or PR -A/PR-\nB balance. Future s tudies incorporating paired \nendometrial analysis are needed to clarify systemic -\nlocal relationships. Although sufficient for detecting \ngroup differences, the sample size may limit \ngeneralizability and reduce power in multivariable \nanalyses, as reflected by attenuation after adjustment. \nThe absence of functional assays further limits \nmechanistic interpretation of the results of the present \nstudy. \n \n5. Conclusion \nIn conclusion, chronic inflammation (elevated IL -6) \nand hyperprolactinemia are associated with reduced \n\nBobby Joseph, et al. 828 \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \nProgesterone Receptor (PGR) gene expression in \nwomen with RPL and endometriosis, potentially \nimpairing progesterone signaling and contributing to \nimplantation failure. The stronger association between \nIL-6 and PGR expression suggests a closer link with \ninflammatory pathways, while prolactin independently \nincreases disease risk. These findings highlight \nsystemic alterations in PGR expression and support an \nintegrated immuno-endocrine model beyond localized \nendometrial mechanisms. \n \n6. Declarations \nAcknowledgments \nThe authors would like to sincerely appreciate the \nsupport and resources provided by Meenakshi \nAcademy of Higher Education and Research, Chennai, \nTamil Nadu, India, and Genetika, Centre for Advanced \nGenetic Studies, Thiruvananthapuram, Kerala, India. \n \nEthical Considerations \nThe study protocol was approved by the Institutional \nEthics Committee of Genetika  (Reg. No. \nEC/NEW/INST/2025/KL/0661; Approval No: \n19/2023/IECG), and all procedures adhered to the \nethical standards outlined in the Declaration of \nHelsinki (1964 and its later amendments). \n \nAuthors' Contributions \nB.J was responsible for conceptualization, data \ncuration, investigation, methodology, and original draft \nwriting. B.K, A.S, D.R.D provided supervision, project \nadministration, and resources, guiding the overall \ndirection and execution of the study. A.S.D.R .D, A.R \nand J.J.S.N. contributed to formal analysis and \nvalidation. F.O and S.U supported data curation and \ninvestigation. Review and editing were overseen by \nA.S and D.R.D. All the authors read and approved the \nfinal manuscript. \n \nConflict of Interest \nNo potential conflict of interest was reported by the \nauthors. \n \nFund or Financial Support \nThis research received no external funding. \n \n \n \n \n \n \n1. Eshre Guideline Group on RPL, Bender Atik R, \nChristiansen OB, Elson J, Kolte AM, Lewis S, \net al. ESHRE guideline: recurrent pregnancy \nloss: an update in 2022. Hum Reprod Open. \n2023;2023(1):hoad002. \n[doi:10.1093/hropen/hoad002] \n2. Sudharma D, Muninathan N, Suresh A, \nParthasarathy M, Krishnakumary AR, \nMullukalayil Joseph S, et al. Synergistic Effects \nof IL -16 and KRAS in Endometriosis with \nEmphasis on Oxidative Stress. J Obstet Gynecol \nCancer Res. 2026;11(4):358 -66. \n[doi:10.24200/jogcr.11.4.358] \n3. Laijawala RA. Recurrent Pregnancy Loss: \nImmunological aetiologies and associations \nwith mental health. Brain Behav Immun Health. \n2024;41:100868. \n[doi:10.1016/j.bbih.2024.100868] \n4. Garmendia JV, De Sanctis CV, Hajdúch M, De \nSanctis JB. Endometriosis: an immunologist’s \nperspective. Int J Mol Sci. 2025;26(11):5193. \n[doi:10.3390/ijms26115193] \n5. Vaisbuch E, Erez O, Romero R. Physiology of \nprogesterone. In: Progestogens in Obstetrics and \nGynecology. Cham: Springer International \nPublishing; 2021:1 -29. [ doi:10.1007/978-3-\n030-52508-8_1] \n6. Gomaa IA, Sabry A, Allam IS, Ashoush S, Reda \nA. Endometrial progesterone and estrogen \nreceptors in relation to hormonal levels in \nwomen with unexplained recurrent miscarriage. \nRev Bras Ginecol Obstet. 2023;45(11):e676-82. \n[doi:10.1055/s-0043-1776030] \n7. Ye Z, Meng Q, Zhang W, He J, Zhao H, Yu C, \net al. Exploration of the shared gene and \nmolecular mechanisms between endometriosis \nand recurrent pregnancy loss. Front Vet Sci. \n2022;9:867405. \n[doi:10.3389/fvets.2022.867405] \n8. Incognito GG, Di Guardo F, Gulino FA, \nGenovese F, Benvenuto D, Lello C, et al. \nInterleukin-6 as a useful predictor of \nendometriosis-associated infertility: a \nsystematic review. Int J Fertil Steril. \n2023;17(4):226-30. \n[doi:10.22074/ijfs.2023.557683.1329] \n9. Moustakli E, Potiris A, Zikopoulos A, Drakaki \nE, Arkoulis I, Skentou C, et al. Immunological \nReferences \n\n829 Prolactin and PGR Expression in Endometriosis RPL \n      Volume 11, September 2026       Journal of Obstetrics, Gynecology and Cancer Research \nfactors in recurrent pregnancy loss: \nmechanisms, controversies, and emerging \ntherapies. Biology (Basel). 2025;14(7):877. \n[doi:10.3390/biology14070877] \n10. Presicce P, Roland C, Senthamaraikannan P, \nCappelletti M, Hammons M, Miller LA, et al. \nIL-1 and TNF mediates IL -6 signaling at the \nmaternal-fetal interface during intrauterine \ninflammation. Front Immunol. \n2024;15:1416162. \n[doi:10.3389/fimmu.2024.1416162] \n11. 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Abuhassan Q, Oriquat G, Menon SV, Chanania \nK, Inbathamizh  L, Mukherjee G, et al. The \nmolecular puzzle of recurrent implantation \nfailure: integrating genetic, epigenetic, and \nimmune mechanisms for precision reproductive \nmedicine. J Assist Reprod Genet. \n2026;43(5):1349-63. [doi:10.1007/s10815-026-\n03832-2] \n \nHow to Cite This Article:  \nJoseph B, Kanniappan  B, Roy Divakaran  D, Suresh A, Sundaresh A, Rajan A, et al . Systemic Inflammation and \nHyperprolactinemia as Determinants of Progesterone Receptor Gene Expression in Recurrent Pregnancy Loss with \nEndometriosis. J Obstet Gynecol Cancer Res. 2026;11(9):820-829. \nDownload citation:                             RIS | EndNote | Mendeley |BibTeX |","source_license":"CC0","license_restricted":false}