Abstract
Background
The Systemic Inflammatory Response Index (SIRI) is a novel prognostic biomarker based on peripheral blood counts of neutrophils, monocytes, and lymphocytes. Recent evidence suggests that it is associated with poor prognosis in various cancers. However, the predictive value of the SIRI in non‐muscle‐invasive bladder cancer (NMIBC) patients treated with intravesical Bacillus Calmette‐Guerin (BCG) immunotherapy remains elusive. Therefore, this study aimed to evaluate the potential of SIRI as a prognostic factor in these patients.
Methods
A total of 540 patients with NMIBC who underwent BCG immunotherapy following transurethral resection of bladder tumor (TURBT) were enrolled in this study. Using receiver operating characteristic (ROC) curves and the Youden index, patients were divided into high and low SIRI groups based on the cutoff values. Univariable and multivariable logistic regression analyses were performed to identify independent predictors of BCG non‐response. Thereafter, propensity score matching (PSM) was used to eliminate bias due to confounding factors between the low and high SIRI groups. Finally, the Kaplan–Meier method was used to compare recurrence‐free survival (RFS) and progression‐free survival (PFS) between the two groups.
Results
Multivariable logistic regression analysis revealed that high SIRI (p = 0.001), high MLR (p = 0.015), and high tumor pathological T stage (p = 0.015) were significantly correlated with non‐response to BCG therapy. In addition, both RFS and PFS were shorter in the high SIRI group than in the other group before and after PSM (both p < 0.05). Collectively, our results indicate that the combination of tumor pathological T staging and the SIRI can enhance the predictive power of BCG response.
Conclusion
Pretreatment peripheral blood SIRI can be employed to predict the response to BCG immunotherapy and the prognosis of NMIBC patients. Taken together, the combination of T stage and SIRI demonstrated robust performance in predicting the response to BCG immunotherapy in NMIBC patients.
Keywords: BCG response, biomarkers, inflammation, NMIBC, systemic inflammatory response index
The Systemic Inflammatory Response Index (SIRI) is a novel prognostic biomarker based on peripheral blood counts of neutrophils, monocytes, and lymphocytes. However, the predictive value of the SIRI in non‐muscle‐invasive bladder cancer (NMIBC) patients treated with intravesical Bacillus Calmette‐Guerin (BCG) immunotherapy remains elusive. Therefore, this study aimed to evaluate the potential of SIRI as a prognostic factor in these patients. A total of 540 patients with NMIBC who underwent BCG immunotherapy following transurethral resection of bladder tumor (TURBT) were enrolled in this study. Using receiver operating characteristic (ROC) curves and the Youden index, patients were divided into high and low SIRI groups based on the cutoff values. Univariable and multivariable logistic regression analyses were performed to identify independent predictors of BCG non‐response. Thereafter, propensity score matching (PSM) was used to eliminate bias due to confounding factors between the low and high SIRI groups. Finally, the Kaplan‐Meier method was used to compare recurrence‐free survival (RFS) and progression‐free survival (PFS) between the two groups. Multivariable logistic regression analysis revealed that high SIRI (p = 0.001), high MLR (p = 0.015), and high tumor pathological T stage (p = 0.015) were significantly correlated with non‐response to BCG therapy. In addition, both RFS and PFS were shorter in the high SIRI group than in the other group before and after PSM (both p < 0.05). Pretreatment peripheral blood SIRI can be employed to predict the response to BCG immunotherapy and the prognosis of NMIBC patients. The combination of T stage and SIRI demonstrated robust performance in predicting the response to BCG immunotherapy in NMIBC patients.
1. INTRODUCTION
Bladder cancer is one of the most common malignancies of the genitourinary system and is divided into the following two main types 1 : Non‐muscle‐invasive bladder cancer (NMIBC) and muscle‐invasive bladder cancer (MIBC). The former includes Ta and T1 stages, whereas the latter comprises T2, T3, and T4 stages. 2 Pathologically, bladder cancer includes urothelial carcinoma and other histological types (squamous cell carcinoma, adenocarcinoma, small cell carcinoma, etc.), with urothelial carcinoma accounting for 75% of cases. 3 At present, the treatment of NMIBC is predominantly based on transurethral resection of bladder tumor (TURBT), followed by intravesical instillation therapy (including chemotherapy and immunotherapy), with Bacille Calmette‐Guérin (BCG) being the most commonly used drug for immunotherapy. 4 However, up to 80% of patients might suffer from disease recurrence after treatment, and approximately 40% of them might progress to muscle‐invasive disease. The latest EAU guidelines recommend intravesical BCG immunotherapy for high‐risk NMIBC patients. 5 However, its efficacy therapy remains limited, and patients with disease recurrence and progression often have to undergo radical cystectomy (RC). Therefore, it is imperative to identify new biomarkers to predict BCG treatment efficacy and disease prognosis in order to prevent the continuation of ineffective BCG therapy.
A previous study indicated that the inflammatory reaction and immune surveillance play a critical role in tumor initiation, progression, and prognosis and that patients can be distinguished by alterations in the number of immune cells in the peripheral blood. 6 Different clinical indicators in the peripheral blood, such as the absolute counts of monocytes, lymphocytes, and neutrophils, have been explored as promising prognostic indicators in different tumor models, while others have been converted into ratios, such as the platelet‐to‐lymphocyte ratio (PLR), neutrophil‐to‐lymphocyte ratio (NLR), and monocyte‐to‐lymphocyte ratio (MLR). 7 , 8 , 9 , 10 As is well‐documented, low lymphocyte counts and high neutrophil and/or monocyte counts may impact prognosis in various types of cancer.
To date, studies on the predictive value of inflammatory markers in NMIBC patients receiving intravesical BCG immunotherapy are scarce. In this study, we thus evaluated a novel inflammatory response index that integrated neutrophils, monocytes, and lymphocytes, termed the systemic inflammation response index (SIRI), which has been reported to have a potential predictive role in different cancers. 11 , 12 Considering that there was no report on the use of the SIRI in NMIBC patients undergoing intravesical BCG immunotherapy, this study aimed to assess its prognostic value in this patient population.
2. MATERIALS AND METHODS
2.1. Patient selection
This retrospective study included a total of 1335 NMIBC patients who underwent intravesical BCG immunotherapy following transurethral resection of bladder tumor (TURBT) between January 2016 and December 2020 at the Second Xiangya Hospital of Central South University. The inclusion criteria were as follows: (1) patients aged >18 years, (2) pathological examination confirmed the diagnosis of bladder urothelial carcinoma without muscle invasion, (3) patients that have undergone intravesical BCG instillation after TURBT, (4) BCG therapy was well‐tolerated without any treatment interruption, and (5) complete baseline characteristics were available. The exclusion criteria were as follows: (1) history of other malignant tumors (n = 35), (2) lack of follow‐up data (n = 518), (3) history of chemotherapy or radiotherapy (n = 156), and (4) active infection or inflammatory disease (n = 86). Ultimately, this study enrolled 540 patients. A flowchart of the population is illustrated in Figure 1. This retrospective study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University and was performed in accordance with the Declaration of Helsinki. Owing to the retrospective nature of this study and the patient data being anonymized and deidentified prior to analysis, written informed consent was not required.
FIGURE 1.
Diagram of the study population
2.2. Clinical variables
The variables collected in this study were as follows: age, sex, smoking status, diabetes status, hypertension status, tumor recurrence, tumor progression, BCG response, and primary tumor characteristics (pathological T stage, pathological grade, lymphovascular infiltration (LVI), carcinoma in situ (CIS), tumor number, and tumor size). Hematological data, such as neutrophil, monocyte, and lymphocyte counts, were acquired following blood collection within 3 days before surgery to calculate the PLR, NLR, MLR, and SIRI. PLR, NLR, MLR, and SIRI were calculated using the following formulas: PLR = platelet count/lymphocyte count, NLR = neutrophil count/lymphocyte count, MLR = monocyte count/lymphocyte count, SIRI = neutrophil count × monocyte count/lymphocyte count.
2.3. Patient follow‐up
Discharged patients were closely followed up to monitor disease recurrence, progression, and BCG response. Patient follow‐up included cystoscopy, blood and urine cytology, and abdominal computed tomography (CT). The BCG immunotherapy schedule was as follows: 120 mg weekly during the initial induction period for a total of 6 times, then 120 mg once every 2 weeks for a total of 3 intensive cycles, and finally, a monthly maintenance dose of 120 mg for a total of 10 times. The treatment period was 1 year, with a total of 19 sessions. Non‐response to BCG treatment was defined as follows: (1) disease recurrence within 6 months of non‐interrupted BCG treatment; (2) developing CIS within 1 year of BCG treatment. 13 Disease recurrence was defined as tumor recurrence in the urinary tract in patients who underwent TURBT. Disease progression was defined as an increase in the TNM stage of the tumor; or an increase in the tumor grade. 14 Recurrence‐free survival (RFS) was defined as the time from the initial TURBT to the date of disease recurrence or last follow‐up (December 2020). Progression‐free survival (PFS) was defined as the time from the initial TURBT to disease progression or last follow‐up (December 2020).
2.4. Statistical analysis
Statistical analyses were performed using the SPSS 26.0 statistical software. Non‐normal continuous variables were expressed as medians (interquartile range values), whereas categorical variables were expressed as frequencies (frequency). Nonparametric tests were used to analyze non‐normal continuous variables, while Pearson's chi‐square tests were used to analyze categorical variables. Univariable and multivariable logistic regression analyses were employed to determine predictors of BCG treatment response. The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff values for the PLR, NLR, MLR, and SIRI in screening for BCG response in NMIBC patients based on the maximal Youden index. In addition, propensity score matching (PSM) was used to eliminate bias due to confounding factors between the low and high SIRI groups. The low and high SIRI groups were matched 1:1 using a match tolerance of 0.02 for the propensity score through caliper matching. 15 The differences in RFS and PFS before and after PSM were compared between the two groups using the Kaplan–Meier method. The parameters included the mean survival time, median survival time, and 95% confidence interval (95% CI). A two‐tailed p value of <0.05 was considered statistically significant.
3. RESULTS
3.1. Clinicopathological features
A total of 540 NMIBC patients who underwent surgical treatment were included in this study, including 340 (63%) males and 200 (37%) females, with a median age of 63 (53–70) years. TNM staging identified 251 cases in the Ta stage and 289 in the T1 stage. 145 patients were identified as BCG non‐responders. The detailed clinical, pathological, and demographic characteristics are summarized in Table 1.
TABLE 1.
Clinical characteristics of the patients according to the SIRI
| Characteristics | Total (n = 540) | Low SIRI (n = 231) | High SIRI (n = 309) | p value |
|---|---|---|---|---|
| Age (years) | 63 (53–70) | 60 (52–69) | 64 (55–71) | 0.005 |
| Sex (n, %) | ||||
| Male | 340 (63) | 139 (60.2) | 201 (65) | 0.246 |
| Famale | 200 (37) | 92 (39.8) | 108 (35) | |
| Smoking status (n, %) | 0.002 | |||
| Never | 239 (44.3) | 120 (51.9) | 119 (38.5) | |
| Yes | 301 (55.7) | 111 (48.1) | 190 (61.5) | |
| Diabetes (n, %) | 0.132 | |||
| No | 478 (88.5) | 210 (90.9) | 268 (86.7) | |
| Yes | 62 (11.5) | 21 (9.1) | 41 (13.3) | |
| Hypertension (n, %) | 0.129 | |||
| No | 386 (71.5) | 173 (74.9) | 213 (68.9) | |
| Yes | 154 (28.5) | 58 (25.1) | 96 (31.1) | |
| Tumor size (n, %) | 0.003 | |||
| <3 cm | 295 (54.6) | 143 (61.9) | 152 (49.2) | |
| ≥3 cm | 245 (45.4) | 88 (38.1) | 157 (50.8) | |
| Tumor number (n, %) | 0.002 | |||
| Single | 367 (68) | 140 (60.6) | 227 (73.5) | |
| Multiple | 173 (32) | 91 (39.4) | 82 (26.5) | |
| Pathologic T stage (n, %) | 0.326 | |||
| pTa | 251 (46.5) | 113 (48.9) | 138 (44.7) | |
| pT1 | 289 (53.5) | 118 (51.1) | 171 (55.3) | |
| Pathologic grade (n, %) | 0.064 | |||
| Low grade | 279 (51.7) | 130 (56.3) | 149 (48.2) | |
| High grade | 261 (48.3) | 101 (43.7) | 160 (51.8) | |
| BCG response (n, %) | <0.001 | |||
| Non‐objective response | 145 (26.9) | 24 (10.4) | 121 (39.2) | |
| Objective response | 395 (73.1) | 207 (89.6) | 188 (60.8) | |
| Concomitant CIS (n, %) | 0.003 | |||
| No | 504 (93.3) | 224 (97) | 280 (90.6) | |
| Yes | 36 (6.7) | 7 (3) | 29 (9.4) | |
| LVI (n, %) | 0.125 | |||
| Absent | 504 (93.3) | 220 (95.2) | 284 (91.9) | |
| Present | 36 (6.7) | 11 (4.8) | 25 (8.1) | |
Abbreviations: BCG, Bacillus Calmette‐Guerin; CIS, carcinoma in situ; LVI, lymphovascular infiltration; SIRI, systemic inflammation response index.
3.2. Potential predictive factors of BCG response
145 (26.9%) patients were classified as BCG non‐responders, while 395 (73.1%) patients were identified as BCG responders during the follow‐up period. The optimal cutoff values for the PLR, NLR, MLR, and SIRI were 123.4398, 3.0435, 0.1995, and 0.785, respectively. Meanwhile, the area under the curves (AUCs) for PLR, NLR, MLR, and SIRI were 0.592 (sensitivity, Sen: 75.9%; specificity, Sep: 42.5%), 0.616 (sensitivity, Sen: 51%; specificity, Sep: 72.2%), 0.663 (sensitivity, Sen: 85.5%; specificity, Sep: 47.1%), and 0.679 (sensitivity, Sen: 83.4%; specificity, Sep: 52.4%), respectively (Figure 2). Notably, univariable analysis revealed that BCG non‐response was correlated with tumor size (p = 0.01), pathological T stage (p = 0.005), concomitant CIS (p = 0.006), PLR > 123.4398 (p < 0.001), NLR > 3.0435 (p < 0.001), MLR > 0.1995 (p < 0.001), and SIRI >0.785 (p < 0.001). Afterward, using the significant risk factors identified in the univariable analysis, a forward stepwise multivariable analysis identified independent predictors associated with BCG non‐response, including pathologic T stage (p = 0.015; OR = 1.776, 95% CI 1.164–2.711), MLR > 0.1995 (p = 0.015; OR = 2.229, 95% CI 1.172–4.238), and SIRI >0.785 (p = 0.001; OR = 3.139, 95% CI 1.701–5.792) (Table 2).
FIGURE 2.
Comparison of the AUCs for PLR, NLR, MLR, and SIRI in predicting treatment response to intravesical BCG immunotherapy. AUCs, area under curves; BCG, Bacille Calmette‐Guérin; MLR, monocyte‐to‐lymphocyte; NLR, neutrophil‐to‐lymphocyte ratio; PLR, platelet‐to‐lymphocyte ratio; SIRI, systemic inflammation response index
TABLE 2.
Assessment of potential risk factors for BCG response
| Characteristics | BCG responders (n = 395) | BCG non‐responders (n = 145) | Univariable | Multivariable | ||||
|---|---|---|---|---|---|---|---|---|
| OR | 95%CI | p value | OR | 95%CI | p value | |||
| Age (years) | 62 (53–70) | 64 (55–71) | 1.014 | 0.998–1.031 | 0.09 | |||
| Sex (n, %) | 1.212 | 0.813–1.808 | 0.345 | |||||
| Male | 244 (61.8) | 96 (66.2) | ||||||
| Female | 151 (38.2) | 49 (33.8) | ||||||
| Smoking status (n, %) | 1.427 | 0.967–2.106 | 0.073 | |||||
| Never | 184 (46.6) | 55 (37.9) | ||||||
| Current or ex‐smoker | 211 (53.4) | 90 (62.1) | ||||||
| Diabetes (n, %) | 1.234 | 0.693–2.198 | 0.474 | |||||
| No | 352 (89.1) | 126 (86.9) | ||||||
| Yes | 43 (10.9) | 19 (13.1) | ||||||
| Hypertension (n, %) | 0.939 | 0.614–1.435 | 0.771 | |||||
| No | 281 (71.1) | 105 (72.4) | ||||||
| Yes | 114 (28.9) | 40 (27.6) | ||||||
| Tumor size (n, %) | 1.651 | 1.126–2.422 | 0.01 | 1.223 | 0.798–1.876 | 0.355 | ||
| <3 cm | 229 (58) | 66 (45.5) | ||||||
| ≥3 cm | 166 (42) | 79 (54.5) | ||||||
| Tumor number (n, %) | 0.859 | 0.568–1.3 | 0.473 | |||||
| Single | 265 (67.1) | 102 (70.3) | ||||||
| Multiple | 130 (32.9) | 43 (29.7) | ||||||
| Pathologic T stage (n, %) | 1.745 | 1.18–2.58 | 0.005 | 1.776 | 1.164–2.711 | 0.015 | ||
| pTa | 198 (50.1) | 53 (36.6) | ||||||
| pT1 | 197 (49.9) | 92 (63.4) | ||||||
| Pathologic grade (n, %) | 1.159 | 0.792–1.697 | 0.447 | |||||
| Low grade | 208 (52.7) | 71 (49) | ||||||
| High grade | 187 (47.3) | 74 (51) | ||||||
| Concomitant CIS (n, %) | 2.628 | 1.326–5.211 | 0.006 | 1.821 | 0.856–3.871 | 0.119 | ||
| No | 376 (95.2) | 128 (88.3) | ||||||
| Yes | 19 (4.8) | 17 (11.7) | ||||||
| LVI (n, %) | 1.215 | 0.582–2.537 | 0.604 | |||||
| Absent | 370 (93.7) | 134 (92.4) | ||||||
| Present | 25 (6.3) | 11 (7.6) | ||||||
| PLR (n, %) | 2.326 | 1.514–3.574 | <0.001 | 1.436 | 0.872–2.364 | 0.155 | ||
| ≤123.4398 | 168 (42.5) | 35 (24.1) | ||||||
| >123.4398 | 227 (57.5) | 110 (75.9) | ||||||
| NLR (n, %) | 2.7 | 1.823–4 | <0.001 | 0.984 | 0.607–1.595 | 0.947 | ||
| ≤3.0435 | 285 (72.2) | 71 (49) | ||||||
| >3.0435 | 110 (27.8) | 74 (51) | ||||||
| MLR (n, %) | 5.255 | 3.178–8.689 | <0.001 | 2.229 | 1.172–4.238 | 0.015 | ||
| ≤0.1995 | 186 (47.1) | 21 (14.5) | ||||||
| >0.1995 | 209 (52.9) | 124 (85.5) | ||||||
| SIRI (n, %) | 5.551 | 3.434–8.975 | <0.001 | 3.139 | 1.701–5.792 | 0.001 | ||
| ≤0.785 | 207 (52.4) | 24 (16.6) | ||||||
| >0.785 | 188 (47.6) | 121 (83.4) | ||||||
Abbreviations: BCG, Bacillus Calmette‐Guerin; CIS, carcinoma in situ; LVI, lymphovascular infiltration; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SIRI, systemic inflammation response index.
3.3. Impact of low and high SIRI on the RFS and PFS
According to the results of the logistic regression model, the PLR, NLR, MLR, and SIRI were associated with BCG treatment response in the univariable analysis, whereas only the SIRI and MLR were significantly correlated with BCG response in the univariable analysis; interestingly, the SIRI had a lower P value than the MLR. Therefore, Kaplan–Meier analysis was performed to analyze whether there were differences in RFS or PFS between patients with a low or high SIRI. There were 231 patients (42.8%) with a low SIRI and 309 patients (57.2%) with a high SIRI. Compared with low SIRI patients, those with a high SIRI were typically older, smoked more, had a higher tumor number, a larger tumor volume, and were more likely to develop concomitant CIS and BCG non‐response (Table 1). The Kaplan–Meier survival curves for the RFS and PFS of the two groups are depicted in Figure 3. Compared with the low SIRI group, the high SIRI group had significantly shorter median RFS (low SIRI, 1630.8 days [95% CI 1568.6–1693.1] vs. high SIRI, 990.5 days [95% CI 896.8–1084.2]; p < 0.001) (Figure 3A) and PFS times (low SIRI, 1726.8 days [95% CI 1685.8–1767.8] vs. high SIRI, 1261.6 days [95% CI 1174.4–1348.8]; p < 0.001) (Figure 3B). Furthermore, the results after PSM were as follows: A total of 334 patients (167 patients in each group) were included after PSM. The baseline characteristics of the two cohorts were comparable after PSM (Table 3). Consistent with the results of the Kaplan–Meier analysis, the high SIRI group had significantly shorter median RFS (low SIRI, 1602.5 days [95% CI 1523.3–1681.7] vs. high SIRI, 1046.4 days [95% CI 916.2–1176.6]; p < 0.001) (Figure 4A) and PFS times (low SIRI, 1719.2 days [95% CI 1668.4–1770.1] vs. high SIRI, 1290.0 days [95% CI 1173.1–1406.9]; p < 0.001) compared with the low SIRI group (Figure 4B).
FIGURE 3.
Kaplan–Meier curves and log‐rank tests were used to compare RFS (A) and PFS (B) between high SIRI and low SIRI groups before PSM. CI, confidence interval; HR, hazard ratio; PFS, progression‐free survival; PSM, propensity score matching; RFS, recurrence‐free survival; SIRI, systemic inflammation response index
TABLE 3.
Clinical characteristics of the patients according to the SIRI after Propensity Score Matching
| Characteristics | Total (n = 334) | Low SIRI (n = 167) | High SIRI (n = 167) | p value |
|---|---|---|---|---|
| Age (years) | 61 (52.75–70) | 61 (53–69) | 62 (51–70) | 1 |
| Sex (n, %) | ||||
| Male | 212 (63.5) | 101 (60.5) | 111 (66.5) | 0.256 |
| Female | 122 (36.5) | 66 (39.5) | 56 (33.5) | |
| Smoking status (n, %) | 0.912 | |||
| Never | 145 (43.4) | 73 (43.7) | 72 (43.1) | |
| Yes | 189 (56.6) | 94 (56.3) | 95 (56.9) | |
| Diabetes (n, %) | 0.5 | |||
| No | 294 (88) | 149 (89.2) | 145 (86.8) | |
| Yes | 40 (12) | 18 (10.8) | 22 (13.2) | |
| Hypertension (n, %) | 0.468 | |||
| No | 238 (71.3) | 122 (73.1) | 116 (69.5) | |
| Yes | 96 (28.7) | 45 (26.9) | 51 (30.5) | |
| Tumor size (n, %) | 0.511 | |||
| <3 cm | 178 (53.3) | 86 (51.5) | 92 (55.1) | |
| ≥3 cm | 156 (46.7) | 81 (48.5) | 75 (44.9) | |
| Tumor number (n, %) | 0.633 | |||
| Single | 234 (70.1) | 115 (68.9) | 119 (71.3) | |
| Multiple | 100 (29.9) | 52 (31.1) | 48 (28.7) | |
| Pathologic T stage (n, %) | 0.444 | |||
| pTa | 163 (48.8) | 85 (50.9) | 78 (46.7) | |
| pT1 | 171 (51.2) | 82 (49.1) | 89 (53.3) | |
| Pathologic grade (n, %) | 0.584 | |||
| Low grade | 175 (52.4) | 90 (53.9) | 85 (50.9) | |
| High grade | 159 (47.6) | 77 (46.1) | 82 (49.1) | |
| BCG response (n, %) | <0.001 | |||
| Non‐objective response | 78 (23.4) | 20 (12) | 58 (34.7) | |
| Objective response | 256 (76.6) | 147 (88) | 109 (65.3) | |
| Concomitant CIS (n, %) | 0.078 | |||
| No | 312 (93.4) | 160 (95.8) | 152 (91) | |
| Yes | 22 (6.6) | 7 (4.2) | 15 (9) | |
| LVI (n, %) | 1 | |||
| Absent | 312 (93.4) | 156 (93.4) | 156 (93.4) | |
| Present | 22 (6.6) | 11 (6.6) | 11 (6.6) | |
Abbreviations: BCG, Bacillus Calmette‐Guerin; CIS, carcinoma in situ; LVI, lymphovascular infiltration; SIRI, systemic inflammation response index.
FIGURE 4.
Kaplan–Meier curves and log‐rank tests were used to compare RFS (A) and PFS (B) between high SIRI and low SIRI groups after PSM. CI, confidence interval; HR, hazard ratio; PFS, progression‐free survival; PSM, propensity score matching; RFS, recurrence‐free survival; SIRI, systemic inflammation response index
3.4. Combining SIRI and traditional clinical indicators to predict BCG response
The ROC curves of pathological T stage, pathological T stage combined with the SIRI, and pathological T stage combined with the MLR to predict BCG response are displayed in Figure 5, with AUCs of 0.568 (sensitivity, Sen: 63.4%; specificity, Sep: 50.1%), 0.711 (sensitivity, Sen: 83.4%; specificity, Sep: 52.4%), and 0.701 (sensitivity, Sen: 85.5%; specificity, Sep: 47.1%), respectively (Figure 5). As expected, the combination of pathological T stage and the SIRI demonstrated superior discriminative power in predicting BCG response compared with the other two groups.
FIGURE 5.
Comparison of the AUCs for pathological T stage, pathological T stage combined with the SIRI, and pathological T stage combined with the MLR in predicting BCG response in NMIBC patients. AUCs, area under curves; BCG, Bacille Calmette‐Guérin; MLR, monocyte‐to‐lymphocyte ratio; NMIBC, non‐muscle‐invasive bladder cancer; SIRI, systemic inflammation response index
4. DISCUSSION
In view of the extremely high recurrence rate of NMIBC, several guidelines recommend intravesical BCG therapy for high‐risk NMIBC patients after TURBT. 16 Moreover, numerous prospective randomized trials have corroborated that BCG is effective in preventing tumor recurrence, tumor progression, and mortality. 17 , 18 Nevertheless, patients may still experience disease recurrence following BCG treatment. Therefore, it is crucial to discover effective predictors in clinical practice. In this study, the cutoff value of the SIRI was determined to be 0.785; the AUC for the SIRI was superior to that of other inflammatory markers (PLR, NLR, and MLR). In the univariable and multivariable analyses, a high SIRI value was positively associated with BCG non‐response, whilst survival analyses performed before and after PSM uncovered that a high SIRI may lead to poor RFS and PFS outcomes. Although the cutoff point for the SIRI in predicting the prognosis of different cancers varies, the results of our study are in line with earlier studies that reported that a high SIRI is an independent prognostic indicator for the recurrence and progression of malignant tumors. 12 , 19 , 20
Current research established that inflammation is one of the hallmarks of tumor development and progression. 21 In recent years, a huge body of evidence has shown that cancer growth, recurrence, and progression are influenced by systemic inflammation, thereby affecting patient survival. 22 In some types of cancer, inflammatory conditions are present before the tissue becomes malignant, whereby the cell survival inflammatory environment may contribute to the development of cancer. 23 Indeed, bladder cancer initiation and progression are associated with immune system interactions, and an inflammatory environment may promote tumorigenesis and progression. 24 Some scholars have demonstrated that the changes in regulatory T cells and tumor‐associated macrophages in NMIBC patients were related to poor prognosis. 25 Max Kates et al. found that intravesical BCG immunotherapy induced an increase in CD4+ T cell populations and was more effective than intravesical chemotherapy. 26 Modulating cancer progression by governing the expression of certain inflammatory cytokines has been shown to be a potential therapeutic approach. 27 Therefore, inflammation plays a crucial role in bladder tumor immunosuppression, proliferation, invasion, and metastasis.
The key role of neutrophils in the tumor immune microenvironment has lately become a research hotspot. 28 Mutations in healthy cells are a prerequisite but not sufficient to cause tumorigenesis. However, inflammation plays an important role in initiating tumorigenesis by destroying healthy tissues, and neutrophils participate in this process. 29 They are attracted to different organs via CXCR2 ligands, and their elastase‐derived and immunosuppressive abilities are both involved in tumorigenesis. 30 , 31 , 32 , 33 , 34 Reactive oxygen species generated by neutrophils and angiogenic factors may also be implicated in tumorigenesis, progression, and metastasis. 35 , 36 , 37 Monocytes are recruited throughout different stages of tumorigenesis and development. 38 , 39 , 40 On the one hand, monocyte chemoattractant protein 1 (MCP‐1) exerts chemotactic activity on monocytes in various tumor models and is one of the key chemokines mediating monocyte/macrophage migration and infiltration. 38 , 41 On the other hand, lymphocytes play a crucial role in antitumor immunity and can induce tumor cell apoptosis through cytotoxicity. 42 A decrease in lymphocytes can lead to immune disorders and impede the ability of the immune system to fight tumors. After activation of CD8+ T cells, effector CD8+ cytotoxic T lymphocytes (CTLs) infiltrate the core or invasive site of tumors and kill tumor cells. 43
Mihai et al. described that peripheral blood NLR was associated with the risk of tumor invasion in patients receiving TURBT. 44 In recent years, many scholars have reported that MLR is associated with the prognosis of different cancers. 45 , 46 , 47 Ekaterina Laukhtina et al. found that a higher PLR may lead to lower rates of partial pathological and/or complete pathological response. 48 Nonetheless, the above variables contain only two cell types, whereas the SIRI consists of three different cell types, which may result in superior predictions for bladder urothelial carcinoma patients. According to recent studies, the SIRI has been shown to have an outstanding predictive value in different cancers. Chao et al. found that a higher SIRI was associated with LVI, and further studies concluded that it was associated with survival time in cervical cancer patients. 49 In another study conducted by Hua et al., SIRI was associated with progesterone receptor status through multivariable analysis, thereby affecting the occurrence of breast cancer. 50 Qi et al. postulated that SIRI was associated with survival in pancreatic adenocarcinoma patients receiving chemotherapy. 51 Pancreatic cancer patients with a higher SIRI may lead to poor prognosis. 51 These findings are consistent with ours in that patients with SIRI >0.785 were more prone to be non‐responsive to BCG immunotherapy and had a worse prognosis.
Herein, multivariable analysis demonstrated that the pathological T stage, MLR, and SIRI were independent predictors for BCG non‐response. Another study found that a high MLR was associated with poor prognosis in NMIBC patients treated with BCG, 52 which is consistent with our MLR results. However, we found that the combination of the SIRI and pathological T stage showed superior discriminative power in predicting BCG response than the MLR combined with the pathological T stage. The clinical significance of our study is the identification of a noninvasive, easily accessible, and reproducible prognostic biomarker for predicting BCG response, and the SIRI meets these parameters. For NMIBC patients treated with BCG immunotherapy with a high preoperative SIRI, more intensive follow‐up and monitoring are recommended. Furthermore, tumor T staging in conjunction with the SIRI can enhance prediction efficiency.
Although the results of this study are encouraging, the following limitations should also be taken into account. (1) This study was a single‐center retrospective study, and a large number of patients were excluded due to a lack of follow‐up data or other factors, making the study more susceptible to selection bias. (2) Only patients with urothelial carcinoma were included in this study; other pathological types of bladder cancer were excluded. (3) In this study, preoperative inflammatory indicators were selected. The dynamic fluctuations in the SIRI between time points before and after BCG treatment may provide further information on inflammation and immunity in NMIBC. Therefore, we aim to conduct follow‐up studies to validate our findings. 4 In the future, further multicenter, large sample, prospective clinical trials are warranted to further corroborate our findings.
5. CONCLUSION
In short, the present study demonstrated that a high pretreatment SIRI is an independent risk factor for BCG non‐response and indicative of a poor prognosis in NMIBC patients and may serve as a potential marker for treatment response monitoring. Taken together, the combination of T stage and SIRI demonstrated robust performance in predicting the response to BCG immunotherapy in NMIBC patients.
AUTHOR CONTRIBUTIONS
Kun Ye: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); software (lead); supervision (equal); validation (equal); writing – original draft (lead). Ming Xiao: Conceptualization (equal); data curation (equal); investigation (equal); methodology (supporting); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). Zitaiyu Li: Data curation (supporting); investigation (supporting); methodology (supporting); software (supporting); supervision (supporting); validation (supporting). Kancheng He: Conceptualization (supporting); resources (supporting); software (supporting); supervision (supporting); visualization (supporting). Jinhua Wang: Conceptualization (supporting); formal analysis (supporting); validation (supporting); visualization (supporting). Liang Zhu: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); software (supporting); supervision (supporting); visualization (supporting). Wei Xiong: Investigation (supporting); methodology (supporting); project administration (supporting); supervision (supporting); validation (supporting); visualization (supporting). Zhaohui Zhong: Conceptualization (equal); formal analysis (supporting); project administration (equal); resources (equal); supervision (equal); writing – review and editing (equal). Yuxin Tang: Conceptualization (equal); formal analysis (equal); investigation (equal); project administration (lead); resources (equal); supervision (lead); validation (lead); visualization (equal); writing – review and editing (lead).
ETHICS STATEMENT
The study was approved by the Clinical Research Ethics Committee of The Second Xiangya Hospital of Central South University (XYEYY ‐2021 ‐K019) and adhered to the Declaration of Helsinki.
ACKNOWLEDGMENT
We thank Home for Researchers editorial team (www.home‐for‐researchers.com) for language editing service.
Ye K, Xiao M, Li Z, et al. Preoperative systemic inflammation response index is an independent prognostic marker for BCG immunotherapy in patients with non‐muscle‐invasive bladder cancer. Cancer Med. 2023;12:4206‐4217. doi: 10.1002/cam4.5284
Kun Ye and Ming Xiao contributed equally to this work.
Contributor Information
Zhaohui Zhong, Email: 1595343192@qq.com.
Yuxin Tang, Email: tangyx36@mail.sysu.edu.cn.
DATA AVAILABILITY STATEMENT
The data that support the findings of the study are available from the correponding author upon reasonable request
REFERENCES
- 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394‐424. [DOI] [PubMed] [Google Scholar]
- 2. Lenis AT, Lec PM, Chamie K, Mshs MD. Bladder cancer: a review. JAMA. 2020;324:1980‐1991. [DOI] [PubMed] [Google Scholar]
- 3. Lobo N, Shariat SF, Guo CC, et al. What is the significance of variant histology in urothelial carcinoma? Eur Urol Focus. 2020;6:653‐663. [DOI] [PubMed] [Google Scholar]
- 4. Chang SS, Boorjian SA, Chou R, et al. Diagnosis and treatment of non‐muscle invasive bladder cancer: AUA/SUO guideline. J Urol. 2016;196:1021‐1029. [DOI] [PubMed] [Google Scholar]
- 5. Thiel T, Ryk C, Renström‐Koskela L, et al. Intravesical BCG treatment causes a long‐lasting reduction of recurrence and progression in patients with high‐risk non‐muscle‐invasive bladder cancer. World J Urol. 2019;37:155‐163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Hu M, Xu Q, Yang S, et al. Pretreatment systemic inflammation response index (SIRI) is an independent predictor of survival in unresectable stage III non‐small cell lung cancer treated with chemoradiotherapy: a two‐center retrospective study. Ann Transl Med. 2020;8:1310. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Hong YM, Yoon KT, Hwang TH, Cho M. Pretreatment peripheral neutrophils, lymphocytes and monocytes predict long‐term survival in hepatocellular carcinoma. BMC Cancer. 2020;20:937. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Topkan E, Kucuk A, Ozdemir Y, et al. Systemic inflammation response index predicts survival outcomes in glioblastoma multiforme patients treated with standard stupp protocol. J Immunol Res. 2020;2020:8628540. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Wang C, Wang M, Zhang X, et al. The neutrophil‐to‐lymphocyte ratio is a predictive factor for the survival of patients with hepatocellular carcinoma undergoing transarterial chemoembolization. Ann Transl Med. 2020;8:541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Han D, Zhang J, Zhao J, et al. Platelet‐to‐lymphocyte ratio is an independent predictor of chemoradiotherapy‐related esophageal fistula in esophageal cancer patients. Ann Transl Med. 2020;8:1163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Chen Y, Jin M, Shao Y, Xu G. Prognostic value of the systemic inflammation response index in patients with adenocarcinoma of the oesophagogastric junction: a propensity score‐matched analysis. Dis Markers. 2019;2019:4659048‐4659049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Geng Y, Zhu D, Wu C, et al. A novel systemic inflammation response index (SIRI) for predicting postoperative survival of patients with esophageal squamous cell carcinoma. Int Immunopharmacol. 2018;65:503‐510. [DOI] [PubMed] [Google Scholar]
- 13. Maisch P, Retz M, Gschwend JE, Koll F, Schmid SC. Clinical practice guidelines for bladder cancer: a systematic review and meta‐analysis using the AGREE II instrument. Urol Int. 2021;105:31‐40. [DOI] [PubMed] [Google Scholar]
- 14. Kamat AM, Colombel M, Sundi D, et al. BCG‐unresponsive non‐muscle‐invasive bladder cancer: recommendations from the IBCG. Nat Rev Urol. 2017;14:244‐255. [DOI] [PubMed] [Google Scholar]
- 15. Yao XI, Wang X, Speicher PJ, et al. Reporting and guidelines in propensity score analysis: a systematic review of cancer and cancer surgical studies. J Natl Cancer Inst. 2017;109:djw323. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Álvarez‐Maestro M, Guerrero‐Ramos F, Rodríguez‐Faba O, Domínguez‐Escrig JL, Fernández‐Gómez JM. Current treatments for BCG failure in non‐muscle invasive bladder cancer (NMIBC). Actas Urol Esp. 2021;45:93‐102. [DOI] [PubMed] [Google Scholar]
- 17. Sylvester RJ, van der Meijden APM, Witjes JA, Kurth K. Bacillus calmette‐guerin versus chemotherapy for the intravesical treatment of patients with carcinoma in situ of the bladder: a meta‐analysis of the published results of randomized clinical trials. J Urol. 2005;174:86‐91. [DOI] [PubMed] [Google Scholar]
- 18. Böhle A, Jocham D, Bock PR. Intravesical bacillus Calmette‐Guerin versus mitomycin C for superficial bladder cancer: a formal meta‐analysis of comparative studies on recurrence and toxicity. J Urol. 2003;169:90‐95. [DOI] [PubMed] [Google Scholar]
- 19. Pacheco‐Barcia V, Mondéjar Solís R, France T, et al. A systemic inflammation response index (SIRI) correlates with survival and predicts oncological outcome for mFOLFIRINOX therapy in metastatic pancreatic cancer. Pancreatology. 2020;20:254‐264. [DOI] [PubMed] [Google Scholar]
- 20. Wang T‐C, An T‐Z, Li J‐X, Pang P‐F. Systemic inflammation response index is a prognostic risk factor in patients with hepatocellular carcinoma undergoing TACE. Risk Manag Healthc Policy. 2021;14:2589‐2600. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646‐674. [DOI] [PubMed] [Google Scholar]
- 22. Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer‐related inflammation and treatment effectiveness. Lancet Oncol. 2014;15:e493‐e503. [DOI] [PubMed] [Google Scholar]
- 23. Mantovani A, Allavena P, Sica A, Balkwill F. Cancer‐related inflammation. Nature. 2008;454:436‐444. [DOI] [PubMed] [Google Scholar]
- 24. Wigner P, Grębowski R, Bijak M, Saluk‐Bijak J, Szemraj J. The interplay between oxidative stress, inflammation and angiogenesis in bladder cancer development. Int J Mol Sci. 2021;22:4483. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Miyake M, Tatsumi Y, Gotoh D, et al. Regulatory T cells and tumor‐associated macrophages in the tumor microenvironment in non‐muscle invasive bladder cancer treated with intravesical bacille calmette‐guérin: a long‐term follow‐up study of a Japanese cohort. Int J Mol Sci. 2017;18:2186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Kates M, Nirschl T, Sopko NA, et al. Intravesical BCG induces CD4 T‐cell expansion in an immune competent model of bladder cancer. Cancer Immunol Res. 2017;5:594‐603. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Shadpour P, Zamani M, Aghaalikhani N, Rashtchizadeh N. Inflammatory cytokines in bladder cancer. J Cell Physiol. 2019;234:14489‐14499. [DOI] [PubMed] [Google Scholar]
- 28. Coffelt SB, Wellenstein MD, de Visser KE. Neutrophils in cancer: neutral no more. Nat Rev Cancer. 2016;16:431‐446. [DOI] [PubMed] [Google Scholar]
- 29. Elinav E, Nowarski R, Thaiss CA, Hu B, Jin C, Flavell RA. Inflammation‐induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat Rev Cancer. 2013;13:759‐771. [DOI] [PubMed] [Google Scholar]
- 30. Jamieson T, Clarke M, Steele CW, et al. Inhibition of CXCR2 profoundly suppresses inflammation‐driven and spontaneous tumorigenesis. J Clin Invest. 2012;122:3127‐3144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Popivanova BK, Kitamura K, Wu Y, et al. Blocking TNF‐alpha in mice reduces colorectal carcinogenesis associated with chronic colitis. J Clin Invest. 2008;118:560‐570. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Katoh H, Wang D, Daikoku T, Sun H, Dey SK, DuBois RN. CXCR2‐expressing myeloid‐derived suppressor cells are essential to promote colitis‐associated tumorigenesis. Cancer Cell. 2013;24:631‐644. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Gong L, Cumpian AM, Caetano MS, et al. Promoting effect of neutrophils on lung tumorigenesis is mediated by CXCR2 and neutrophil elastase. Mol Cancer. 2013;12:154. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Houghton AM, Rzymkiewicz DM, Ji H, et al. Neutrophil elastase‐mediated degradation of IRS‐1 accelerates lung tumor growth. Nat Med. 2010;16:219‐223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Deryugina EI, Zajac E, Juncker‐Jensen A, Kupriyanova TA, Welter L, Quigley JP. Tissue‐infiltrating neutrophils constitute the major in vivo source of angiogenesis‐inducing MMP‐9 in the tumor microenvironment. Neoplasia. 2014;16:771‐788. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Coussens LM, Tinkle CL, Hanahan D, Werb Z. MMP‐9 supplied by bone marrow‐derived cells contributes to skin carcinogenesis. Cell. 2000;103:481‐490. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Shojaei F, Singh M, Thompson JD, Ferrara N. Role of Bv8 in neutrophil‐dependent angiogenesis in a transgenic model of cancer progression. Proc Natl Acad Sci USA. 2008;105:2640‐2645. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Franklin RA, Liao W, Sarkar A, et al. The cellular and molecular origin of tumor‐associated macrophages. Science (New York, NY). 2014;344:921‐925. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Movahedi K, Laoui D, Gysemans C, et al. Different tumor microenvironments contain functionally distinct subsets of macrophages derived from Ly6C(high) monocytes. Cancer Res. 2010;70:5728‐5739. [DOI] [PubMed] [Google Scholar]
- 40. Kitamura T, Doughty‐Shenton D, Cassetta L, et al. Monocytes differentiate to immune suppressive precursors of metastasis‐associated macrophages in mouse models of metastatic breast cancer. Front Immunol. 2017;8:2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Chun E, Lavoie S, Michaud M, et al. CCL2 promotes colorectal carcinogenesis by enhancing polymorphonuclear myeloid‐derived suppressor cell population and function. Cell Rep. 2015;12:244‐257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Hoffmann TK, Dworacki G, Tsukihiro T, et al. Spontaneous apoptosis of circulating T lymphocytes in patients with head and neck cancer and its clinical importance. Clinical Cancer Res. 2002;8:2553‐2562. [PubMed] [Google Scholar]
- 43. Farhood B, Najafi M, Mortezaee K. CD8 cytotoxic T lymphocytes in cancer immunotherapy: a review. J Cell Physiol. 2019;234:8509‐8521. [DOI] [PubMed] [Google Scholar]
- 44. Vartolomei MD, Porav‐Hodade D, Ferro M, et al. Prognostic role of pretreatment neutrophil‐to‐lymphocyte ratio (NLR) in patients with non‐muscle‐invasive bladder cancer (NMIBC): a systematic review and meta‐analysis. Urol Oncol. 2018;36:389‐399. [DOI] [PubMed] [Google Scholar]
- 45. Hu R‐J, Ma J‐Y, Hu G. Lymphocyte‐to‐monocyte ratio in pancreatic cancer: prognostic significance and meta‐analysis. Clin Chim Acta. 2018;481:142‐146. [DOI] [PubMed] [Google Scholar]
- 46. Hu G, Liu G, Ma J‐Y, Hu R‐J. Lymphocyte‐to‐monocyte ratio in esophageal squamous cell carcinoma prognosis. Clin Chim Acta. 2018;486:44‐48. [DOI] [PubMed] [Google Scholar]
- 47. Li M, Deng Q, Zhang L, He S, Rong J, Zheng F. The pretreatment lymphocyte to monocyte ratio predicts clinical outcome for patients with urological cancers: a meta‐analysis. Pathol Res Pract. 2019;215:5‐11. [DOI] [PubMed] [Google Scholar]
- 48. Laukhtina E, Pradere B, Mori K, et al. Prognostic blood‐based biomarkers in patients treated with neoadjuvant chemotherapy for urothelial carcinoma of the bladder: a systematic review. Urol Oncol. 2021;39:471‐479. [DOI] [PubMed] [Google Scholar]
- 49. Chao B, Ju X, Zhang L, Xu X, Zhao Y. A novel prognostic marker systemic inflammation response index (SIRI) for operable cervical cancer patients. Front Oncol. 2020;10:766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Hua X, Long Z‐Q, Huang X, et al. The preoperative systemic inflammation response index (SIRI) independently predicts survival in postmenopausal women with breast cancer. Curr Probl Cancer. 2020;44:100560. [DOI] [PubMed] [Google Scholar]
- 51. Qi Q, Zhuang L, Shen Y, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122:2158‐2167. [DOI] [PubMed] [Google Scholar]
- 52. Adamkiewicz M, Bryniarski P, Kowalik M, Burzyński B, Rajwa P, Paradysz A. Lymphocyte‐to‐monocyte ratio is the independent prognostic marker of progression in patients undergoing BCG‐immunotherapy for bladder cancer. Front Oncol. 2021;11:655000. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of the study are available from the correponding author upon reasonable request