Predictive value of peripheral blood systemic immune-inflammation index and blood cell ratio for gastrointestinal polyp burden in patients with Peutz-Jeghers syndrome

Abstract

BACKGROUND

Peutz-Jeghers syndrome (PJS) features gastrointestinal hamartomatous polyps prone to intussusception, obstruction, bleeding, and malignancy. No effective predictors exist for polyp progression. Peripheral blood systemic immune-inflammation index (SII) and blood cell ratios (BCR) correlate with cancer prognosis, but their relationship with PJS polyp size (diameter)/burden (number) remains unknown. This study aims to evaluate the value of this simple and non-invasive method in predicting the polyp burden of PJS.

AIM

To investigate the correlations of SII and peripheral BCR with clinicopathological features in patients with PJS, and to evaluate their predictive value for gastrointestinal polyp burden in PJS.

METHODS

Patients with PJS who met the inclusion and exclusion criteria and were treated at the Air Force Medical Center, Chinese People’s Liberation Army from January 1, 2020, to December 31, 2025, were enrolled in this study. Their clinicopathological data were retrospectively analyzed. Spearman correlation analysis was used to assess the correlations of gastrointestinal polyp size and number with SII, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR). The predictive performance was evaluated using receiver operating characteristic curves.

RESULTS

A total of 636 PJS patients were enrolled in this study. Polyp burden varied across different gastrointestinal segments. SII, NLR, PLR, and MLR could predict the presence of small bowel polyps in PJS patients and were positively correlated with the number of small bowel polyps (r = 0.206, P < 0.001; r = 0.133, P = 0.001; r = 0.128, P = 0.001; r = 0.090, P= 0.024, respectively). MLR was identified as an independent risk factor for malignancy in PJS patients [odds ratio (OR) = 12.186, P = 0.010]. The cumulative risks of cancer in PJS patients at ages 40 years, 50 years, and 60 years were 23.6%, 50.8%, and 66.4%, respectively. The combined model of SII, NLR, PLR, and MLR yielded area under the curve values of 0.779 and 0.774 for predicting large polyps (diameter ≥ 25 mm) and multiple polyps (number ≥ 10), respectively. Multivariate logistic regression analysis showed that PLR (OR = 1.004, 95% confidence interval: 1.001-1.008, P = 0.011) was an independent risk factor for multiple small bowel polyps in PJS patients.

CONCLUSION

The combination of SII, NLR, PLR, and MLR can predict small bowel polyp burden in PJS patients, and holds promise as a non-invasive and cost-effective risk stratification biomarker to facilitate individualized surveillance and early risk intervention in PJS patients.

Key Words: Peutz-Jeghers syndrome; Polyps; Blood cell ratios; Systemic immune-inflammation index

Core Tip: This study aimed to analyze the correlations of the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) with clinicopathological features in patients with Peutz-Jeghers syndrome (PJS). Based on correlation analysis and multivariable logistic regression analysis, we constructed and validated a predictive model incorporating SII, NLR, PLR, and MLR to estimate the size and number of small intestinal polyps in PJS patients. This model holds promise as a non-invasive and cost-effective risk stratification marker, thereby facilitating individualized monitoring and early risk intervention.

INTRODUCTION

Peutz-Jeghers syndrome (PJS) is an autosomal dominant disorder characterized clinically by mucocutaneous pigmentation and multiple hamartomatous polyps throughout the gastrointestinal tract[1,2]. The continuous growth of gastrointestinal polyps in PJS patients can lead to severe complications, including intussusception, obstruction, bleeding, and malignant transformation. The polyp burden (including size and number) in PJS patients is a key determinant of clinical prognosis[3,4].

PJS is characterized by early age of onset, diagnostic and therapeutic challenges, and a protracted disease course. Patients often require repeated hospitalizations for endoscopic polypectomy or surgical intervention, with more than 50% of PJS patients undergoing surgery before the age of 18[5]. Conventional methods for assessing polyp burden in PJS rely on gastroscopy, colonoscopy, and enteroscopy, which are invasive, costly, and associated with a risk of missed diagnosis. Currently, there is a lack of non-invasive and effective markers for predicting gastrointestinal polyp burden in PJS patients, placing substantial physical and psychological burdens on both patients and their families[6].

Studies have shown that the chronic inflammatory microenvironment plays an important role in the development of gastrointestinal polyps. The systemic immune-inflammation index (SII) and various blood cell ratios have been associated with the development and progression of diseases such as hepatocellular carcinoma, gastrointestinal stromal tumors, and colorectal neoplasms[7-9]. In light of prior evidence implicating inflammatory indices in the progression of gastrointestinal malignancies, we proposed that elevated SII, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) correlate with increased polyp load and may further reflect adverse prognostic outcomes in PJS patients. In this study, we analyzed the correlations between peripheral venous blood s SII, NLR, PLR, and MLR and the size and number of gastrointestinal polyps in PJS patients who met the inclusion and exclusion criteria and were admitted to the Air Force Medical Center between January 1, 2020, and December 31, 2025. A predictive model was constructed to evaluate the value of SII, NLR, PLR, and MLR in predicting polyp burden in PJS, with the goal of providing a non-invasive and cost-effective marker for polyp burden prediction in these patients.

MATERIALS AND METHODS

Inclusion criteria

(1) Meeting the diagnostic criteria for PJS[2,10]; (2) Complete medical history, endoscopic findings, and laboratory results; and (3) Signed informed consent.

Exclusion criteria

(1) Suspected PJS, outpatient PJS patients, those not meeting the diagnostic criteria for PJS, or incomplete medical history; (2) Presence of fever, abdominal pain, abdominal distension, infection, or other symptoms at admission; (3) Presence of intussusception, intestinal obstruction, intestinal perforation, or hematologic diseases at admission; (4) Use of nonsteroidal anti-inflammatory drugs, hormones, and/or other immunosuppressive agents, or other medications that may affect routine blood test results within one week before admission; and (5) Refusal to sign the informed consent form.

Data collection

General items: Sex, age, family history, distribution of polyps, number of polyps, and maximum diameter of polyps.

Laboratory tests: Peripheral venous blood routine examination (2 mL of peripheral venous blood collected on the morning after admission while fasting, before endoscopy and before surgery). Recorded parameters included neutrophil count, platelet count, monocyte count, and lymphocyte count. NLR, PLR, MLR, and SII were calculated (SII = platelet count × neutrophil count/Lymphocyte count).

Statistical analysis

Statistical analysis was performed using SPSS 26.0. Measurement data following a normal distribution were expressed as mean ± SD, while skewed data were expressed as median. Count data were expressed as n. For comparisons between groups, the t-test or analysis of variance (ANOVA) was used for normally distributed data with homogeneous variances; the Mann-Whitney U test or Kruskal-Wallis H test was used for non-normally distributed data, data with unequal variances, or ordinal data; the χ² test or Fisher’s exact test was used for count data. Spearman correlation analysis and multivariate logistic regression analysis were performed to assess the associations of SII, NLR, PLR, and MLR with polyp burden. A P value < 0.05 was considered statistically significant.

Bias control

To minimize selection bias, all consecutive PJS patients who met the predefined inclusion and exclusion criteria during the study period were enrolled without preselection. To reduce information bias, clinical data, laboratory parameters, and endoscopic findings were extracted from electronic medical records by two independent investigators using a standardized case report form, with discrepancies resolved by consensus or adjudication by a senior researcher. To control for confounding, multivariable logistic and linear regression models were adjusted for potential confounders, including age, sex, history of abdominal surgery, and use of anti-inflammatory medications. Laboratory measurements were performed blinded to clinical outcomes, and endoscopic evaluations were conducted by experienced gastroenterologists who were unaware of the inflammatory index results at the time of assessment. Furthermore, to mitigate measurement bias, all blood samples were collected under standardized conditions (fasting morning venous blood) and analyzed in the same central laboratory using calibrated equipment.

Ethics statement

This study was approved by the Institutional Review Board of the Air Force Medical Center (Research Ethics Approval No. 2025-95-PJ01). All procedures involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

RESULTS

Baseline characteristics of PJS patients

From January 1, 2020, to December 31, 2025, a total of 651 patients with PJS were admitted to the Air Force Medical Center. According to the inclusion and exclusion criteria, 636 patients were finally enrolled in the study, including 346 males (54.4%) and 290 females (45.6%). The median age at onset of mucocutaneous pigmentation was 2 years (range: 0-33 years). A family history of PJS was present in 270 cases (42.4%), absent in 319 cases (50.2%), and unclear in 47 cases (7.4%). A history of intussusception was reported in 532 patients (83.6%), and 424 patients (66.7%) had undergone abdominal surgery for polyps. Additionally, the polyp burden varied across different segments of the digestive tract in PJS patients (Table 1).

Table 1 The burden of digestive tract polyps in Peutz-Jeghers syndrome patients.

Polyp distribution site	Number and maximum diameter (mm)	Median	Interquartile range	Range (min, max)
Stomach (n = 490)	Number of polyps	10	(5, 10)	1, 100
	Diameter of polyps	10	(6, 15)	2, 80
Small intestine (n = 628)	Number of polyps	10	(5, 16)	1, 150
	Diameter of polyps	40	(25, 50)	3, 160
Colorectum (n = 525)	Number of polyps	7	(3, 11)	1, 147
	Diameter of polyps	25	(15, 40)	3, 120

Correlation analysis of SII, NLR, PLR, and MLR with polyp number and size

Among 636 PJS patients, the median SII was 479.4 [interquartile range (IQR): 336.7-735.0], median NLR was 1.95 (IQR: 1.46-2.73), median PLR was 151.0 (IQR: 117.5-213.3), and median MLR was 0.25 (IQR: 0.19-0.32). Spearman correlation analysis showed that SII, NLR, PLR, and MLR were not significantly correlated with the number or maximum diameter of gastric polyps. However, all four indices were positively correlated with the number of small intestinal polyps, and MLR was also positively correlated with the maximum diameter of small intestinal polyps. SII and NLR were positively correlated with the number of colorectal polyps but showed no correlation with the maximum diameter of colorectal polyps. PLR and MLR were not correlated with either the number or the maximum diameter of colorectal polyps (Figure 1 and Table 2).

Figure 1 Scatter plots of systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio with polyp number. SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio.

Table 2 Correlation analysis of systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio with polyp burden.

Inflammatory indicator	Burden	Stomach	Small intestine	Colorectum
SII	Number	r = 0.032, P = 0.475	r = 0.206, P < 0.001	r = 0.115, P = 0.008
	Diameter	r = -0.020, P = 0.657	r = 0.058, P = 0.148	r = 0.077, P = 0.079
NLR	Number	r = 0.012, P = 0.792	r = 0.133, P = 0.001	r = 0.087, P = 0.047
	Diameter	r = -0.031, P = 0.492	r = 0.028, P = 0.479	r = 0.064, P = 0.144
PLR	Number	r = 0.025, P = 0.583	r = 0.128, P = 0.001	r = 0.023, P = 0.604
	Diameter	r = -0.044, P = 0.333	r = -0.022, P = 0.576	r = 0.004, P = 0.925
MLR	Number	r = 0.031, P = 0.486	r = 0.090, P = 0.024	r = 0.059, P = 0.181
	Diameter	r = -0.028, P = 0.532	r = 0.096, P = 0.017	r = 0.068, P = 0.123

SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio.

Predictive value of SII, NLR, PLR, and MLR for polyps in different anatomical sites and predictive value of small intestinal polyp burden for intussusception

The presence or absence of polyps in the stomach, small intestine, and colorectum was set as the state variable, and preoperative SII, NLR, PLR, and MLR were set as test variables. Receiver operating characteristic (ROC) curves were generated using SPSS 26.0. The area under the curve (AUC) values of SII, NLR, PLR, and MLR for predicting the presence of small intestinal polyps were 0.765, 0.739, 0.745, and 0.694, respectively, with corresponding optimal cut-off values of 351.9, 1.78, 113, and 0.17 (Table 3 and Figure 2A). All four indices showed certain predictive value for predicting the presence of small intestinal polyps in PJS patients (P < 0.001), among which SII had the highest predictive efficacy. In contrast, SII, NLR, PLR, and MLR showed no predictive value for the presence of gastric or colorectal polyps in PJS patients (P > 0.05; Figure 2B and C). The presence or absence of intussusception was set as the state variable, and the number and maximum diameter of small intestinal polyps were set as test variables. The AUC for the number of small intestinal polyps was 0.694, with a corresponding optimal cut-off value of 10 polyps; the AUC for the maximum diameter of small intestinal polyps was 0.638, with a corresponding optimal cut-off value of 25 mm (Table 3 and Figure 2D). Both the number and maximum diameter of small intestinal polyps showed predictive value for the occurrence of intussusception in PJS patients (P < 0.001, P = 0.002).

Figure 2 Receiver operating characteristic curves for systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio in the presence or absence of polyps in different locations. A: Small intestine; B: Stomach; C: Colorectum; D: Receiver operating characteristic curves for polyp burden in the presence or absence of intussusception. SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio.

Table 3 Optimal cutoff, sensitivity, and specificity determined by the maximum Youden index.

Indicator	Cut-off value	Sensitivity (%)	Specificity (%)	AUC (95%CI)	P value
SII	351.9	72.8	87.5	0.765 (0.702-0.828)	< 0.001
NLR	1.78	59.9	87.5	0.739 (0.674-0.804)	< 0.001
PLR	113	78.5	75	0.745 (0.681-0.809)	< 0.001
MLR	0.17	82.8	62.5	0.694 (0.625-0.763)	< 0.001
Number of small intestinal polyps	10	59.4	66.3	0.694 (0.621-0.767)	< 0.001
Diameter of small intestinal polyps (mm)	25	80.3	65.3	0.638 (0.561-0.715)	0.002

SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio; AUC: Area under the curve; CI: Confidence interval.

Group comparisons based on optimal cut-off values

Based on the optimal cut-off values for SII, NLR, PLR, MLR, and the number and maximum diameter of small intestinal polyps as determined by ROC analysis, all PJS patients were divided into high/low SII groups, high/low NLR groups, high/low PLR groups, high/low MLR groups, high/low polyp number groups, and large/small polyp diameter groups (Figure 3). The rank-sum test was performed on the above groups, and the results showed that the SII, NLR, and PLR groupings were significantly associated with both the number and maximum diameter of small intestinal polyps, with higher small intestinal polyp burden in the high SII, NLR, and PLR groups. The MLR grouping showed no statistically significant association with the number of small intestinal polyps (z = -1.898, P = 0.058) but was significantly associated with the maximum diameter of small intestinal polyps (z = -3.737, P < 0.001) (Figure 4). Further reverse validation was performed by grouping patients based on polyp number and maximum diameter to investigate the association between high/low polyp burden and SII, NLR, PLR, and MLR. The results showed that the polyp number grouping was significantly associated with SII (z = -5.048, P < 0.001), NLR (z = -4.797, P < 0.001), PLR (z = -6.11, P < 0.001), and MLR (z = -4.862, P < 0.001). The polyp diameter grouping was also significantly associated with SII (z = -3.161, P = 0.002), NLR (z = -3.187,P = 0.001), PLR (z = -2.774, P = 0.006), and MLR (z = -3.226, P = 0.001). The SII, NLR, PLR, and MLR levels were significantly higher in the high polyp burden groups than in the low polyp burden groups.

Figure 3 Grouping of Peutz-Jeghers syndrome patients based on the optimal cutoff. SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio.

Figure 4 The relationship between high/low systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio groups and the number and diameter of small intestinal polyps. SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio.

Correlation of SII, NLR, PLR, and MLR with malignancy in PJS patients

Among the 636 PJS patients, 59 cases (9.3%) had concurrent malignancies, of which 44 cases (74.58%) were gastrointestinal malignancies (Figure 5). The median age at first diagnosis of malignancy in the 59 patients was 34 years (range: 14-53 years). Eight cancer patients had died, with a median age at death of 34 years. Point-biserial correlation analysis showed that the presence of malignancy in PJS patients was positively correlated with MLR (r = 0.177, P < 0.001) but was not statistically significantly correlated with SII, NLR, or PLR (P > 0.05). Logistic regression further confirmed that MLR [odds ratio (OR) = 12.186, 95% confidence interval (CI): 1.796-82.697, P = 0.010] was an independent risk factor for malignancy in PJS patients. Kaplan-Meier survival analysis was used to estimate the cumulative lifetime risk of cancer in PJS patients, and the results showed that the cumulative risks of cancer at ages 40 years, 50 years, and 60 years were 23.6%, 50.8%, and 66.4%, respectively (Figure 6).

Figure 5  The distribution of malignant tumors in Peutz-Jeghers syndrome patients.

Figure 6  Cumulative risk of cancer in Peutz-Jeghers syndrome patients.

Predictive value of SII, NLR, PLR, and MLR for high-risk polyp characteristics

ROC curve analysis showed that SII, NLR, PLR, and MLR were statistically significant in predicting polyps with different clinical characteristics. For predicting large polyps (diameter ≥ 25 mm), the AUC values of SII, NLR, PLR, and MLR were 0.592 (95%CI: 0.537-0.646, P = 0.002), 0.592 (95%CI: 0.537-0.648, P = 0.001), 0.580 (95%CI: 0.525-0.636, P = 0.006), and 0.594 (95%CI: 0.536-0.651, P = 0.001), respectively. For predicting multiple polyps (number ≥ 10), the AUC values of SII, NLR, PLR, and MLR were 0.617 (95%CI: 0.573-0.661, P < 0.001), 0.611 (95%CI: 0.567-0.655, P < 0.001), 0.642 (95%CI: 0.599-0.685, P < 0.001), and 0.613 (95%CI: 0.569-0.657, P < 0.001), respectively. After combining SII, NLR, PLR, and MLR using logistic regression for joint modeling, the AUC for predicting large polyps increased to 0.779 (95%CI: 0.730-0.827, P < 0.001), and the AUC for predicting multiple polyps increased to 0.774 (95%CI: 0.737-0.811, P < 0.001). The differences were statistically significant compared with using SII, NLR, MLR, and PLR individually for predicting polyp characteristics (P < 0.001) (Figure 7).

Figure 7 The predictive value of the combined application of systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio for Peutz-Jeghers syndrome polyp characteristics. A: Polyp diameter ≥ 25 mm; B: Polyp number ≥ 10. SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio.

Risk factor analysis for high-risk polyps in PJS patients

Using the presence of large polyps and the presence of multiple polyps as dependent variables, respectively, univariate logistic regression analyses were performed with factors including SII, NLR, PLR, MLR, age, family history, age at onset of mucocutaneous pigmentation, history of intussusception, and history of abdominal surgery. Variables with P < 0.1 were then incorporated into multivariate logistic regression models (Table 4). Univariate logistic regression analysis showed that MLR was a risk factor for the presence of large polyps in the small intestine of PJS patients (OR = 6.957, 95%CI: 1.236-39.151, P = 0.028). However, in the multivariate logistic regression analysis, MLR was no longer statistically significant (P > 0.05). Regarding multiple polyps, SII (OR = 1.001, 95%CI: 1.000-1.001, P = 0.003), NLR (OR = 1.151, 95%CI: 1.040-1.274, P = 0.007), and PLR (OR = 1.004, 95%CI: 1.002-1.006, P < 0.001) were risk factors for multiple small intestinal polyps in PJS patients. Among these, PLR remained independently associated after adjusting for confounding factors (OR = 1.004, 95%CI: 1.001-1.008, P = 0.011), whereas SII and NLR were no longer significant (P > 0.05). In addition, a history of intussusception was significantly associated with both the presence of large polyps and the presence of multiple polyps (P < 0.05), and was identified as an important independent risk factor for high-risk polyps in PJS patients.

Table 4 Univariate and multivariate logistic regression analysis of high-risk polyps in Peutz-Jeghers syndrome patients.

Factor	Large polyps (≥ 25 mm)		Multiple polyps (≥ 10)
	P value	Adjusted P value	P value	Adjusted P value
SII	0.302	-	0.003	0.815
NLR	0.749	-	0.007	0.804
PLR	0.467	-	< 0.001	0.012
MLR	0.028	0.053	0.087	0.899
Age	0.1	-	< 0.001	0.036
Family history	0.428	-	0.223	-
Number of intussusception episodes	0.004	0.04	< 0.001	0.004
Number of abdominal surgeries	0.042	0.919	< 0.001	0.133
Age at onset of mucocutaneous pigmentation	0.707	-	0.074	0.15

SII: Systemic immune-inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; MLR: Monocyte-to-lymphocyte ratio.

DISCUSSION

This study demonstrates that small intestinal polyp growth in PJS patients is significantly associated with elevated SII, NLR, MLR, and PLR, suggesting that chronic inflammatory responses may play an important role in the occurrence and development of small intestinal polyps in PJS patients. Nevertheless, it remains unclear whether the observed elevations in systemic inflammatory indices represent a causal driver of polyp growth and progression, or whether they are secondary to increased polyp burden-or both. Further investigations are warranted to establish the temporal and causal relationships between inflammation and polyp biology. While accumulating evidence has implicated inflammation in the pathogenesis of gastrointestinal polyps, the specific mechanisms through which chronic inflammation promotes the initiation and progression of PJS-associated polyps remain poorly understood. For a long time, the genetic basis of PJS has been primarily attributed to loss-of-function mutations in the STK11/LKB1 gene[11,12]. Inactivation of STK11/LKB1 not only disrupts cell polarity and glandular structure but also leads to aberrant activation of the mTOR signaling pathway[13]. Under the synergistic effect of chronic inflammation, cells with STK11/LKB1 loss-of-function exhibit hyperactivation and dysfunctional effector function[14]. Particularly in the intestinal mucosal environment, these cells drive a sustained inflammatory cascade around the polyps[15,16]. Thereby creating a microenvironment that supports polyp growth. This local inflammatory response is not confined to the lesion but also affects the whole body via the circulatory system[15,17,18]. Activated immune cells and released inflammatory cytokines enter the peripheral blood, leading to a significant elevation of systemic inflammatory markers, such as SII, NLR, PLR, MLR, and systemic cytokine levels[19]. These changes in inflammatory levels significantly increase the risk of progression from PJS hamartomatous polyps to dysplasia and adenomatous polyps, ultimately leading to malignant transformation of polyps and the development of gastrointestinal malignancies. However, we emphasize that these mechanistic inferences are speculative and derived from the published literature; they are offered as potential explanatory frameworks for our observed associations, rather than as conclusions established by our study. Drawing upon our clinical findings, we hypothesize that inflammation promotes the progression of PJS polyps along a continuum-from hamartoma to adenoma and ultimately to carcinoma-consistent with an “inflammation-hamartoma-adenoma-carcinoma” paradigm[20]. This perspective broadens the understanding of the carcinogenic mechanism of PJS polyps and highlights the central role of chronic inflammation in the development and progression of PJS polyps.

In this study, 59 PJS patients had concurrent malignancies, of which 35 cases (59.3%) resulted from malignant transformation of PJS polyps. The cumulative risks of cancer at ages 40 years, 50 years, and 60 years in PJS patients were 23.6%, 50.8%, and 66.4%, respectively. Considering the relatively short follow-up period in some cases, the actual cumulative cancer risk may be even higher. Once diagnosed with PJS, it is recommended that patients undergo annual peroral and transanal small bowel enteroscopy starting at 12 years of age, if conditions permit, to assess polyp growth rate and to remove polyps promptly. Lifelong endoscopic surveillance is generally required. Early screening, regular endoscopic monitoring, and lifelong follow-up are crucial to avoid surgical intervention[21-23]. PJS is characterized by early age of onset, protracted and recurrent course, high risks of intussusception and cancer, which severely affect patients' quality of life and impose heavy economic and psychological burdens on patients and their families[24]. Therefore, identifying effective and simple predictive markers for polyp growth and developing drugs to inhibit polyp progression have become urgent priorities. In the present study, SII, NLR, MLR, and PLR were positively correlated with small intestinal polyp burden in PJS patients. Furthermore, MLR was identified as an independent risk factor for malignancy in PJS patients. Moro-Valdezate et al[25] reported that SII and NLR could serve as risk predictors for colorectal neoplastic lesions and were significantly associated with prognosis. Notably, although this study confirmed a positive correlation between these inflammatory markers and the presence of polyps, the correlations were weak and not suitable for independent prediction, and the causal relationship remains to be further clarified. It is possible that inflammation drives polyp formation, or alternatively, that polyps themselves induce secondary systemic inflammatory responses. Moreover, elevations in these markers may also be influenced by infections, metabolic diseases, or other chronic inflammatory conditions[26,27]. Several limitations of this study should be acknowledged. First, the single-center retrospective design may have introduced selection bias, as PJS patients exhibit marked clinical heterogeneity with variable inter-surveillance intervals, potentially leading to admission bias. Second, the cross-sectional design precludes causal inference, as we cannot determine whether elevated inflammatory indices drive polyp formation or result from polyp burden. Third, the modest correlation strength and the lack of external validation limit the clinical utility and generalizability of our findings. Therefore, future studies should adopt multicenter prospective cohort designs to dynamically observe the relationship between changes in inflammatory markers and the development, progression, and malignant transformation of polyps, along with animal experiments to elucidate the interactive mechanisms between inflammation and STK11/LKB1 mutations.

In summary, starting from the "inflammation-hamartoma-adenoma-carcinoma" evolution pathway of PJS polyps, future research should further elucidate the interaction mechanisms between chronic inflammation and genetic mutations such as STK11/LKB1, and explore their associations with specific molecular pathways (e.g., COX-2/PGE2, NF-κB). Targeting the inflammatory microenvironment of PJS polyps for prevention and treatment may enable effective management of gastrointestinal polyps in PJS patients. Based on the results of this study, SII, NLR, MLR, and PLR can effectively predict small intestinal polyp burden in PJS patients and may serve as adjunctive assessment tools for PJS polyp risk, facilitating initial screening and stratified management of PJS polyp risk.

CONCLUSION

SII, NLR, PLR, and MLR have certain predictive value for small intestinal polyp burden in PJS patients. Higher levels of SII, NLR, PLR, and MLR in PJS patients are associated with a greater small intestinal polyp burden. These markers hold promise as reference indicators for risk stratification in patients with PJS.