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Inflammatory Biomarkers and Bladder Cancer Prognosis: A Systematic Review

European Urology, 6, 66, pages 1078 - 1091



Host immune response has an impact on tumour development and progression. There is interest in the use of inflammatory biomarkers (InfBMs) in cancer care. Although several studies assessing the potential prognostic value of InfBMs in cancer have been published in the past decades, they have had no impact on the management of patients with urothelial bladder carcinoma (UBC).


To review and summarise the scientific literature on the prognostic value of tumour, serum, urine, and germline DNA InfBMs on UBC.

Evidence acquisition

A systematic review of the literature was performed searching the Medline and Embase databases for original articles published between January 1975 and November 2013. The main inclusion criterion was the provision of a survival analysis (Kaplan-Meier and/or Cox) according to the Reporting Recommendations for Tumor Marker Prognostic Studies guidelines for the assessment of prognostic markers. We focused on markers assessed at least twice in the literature. Findings are reported following Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines.

Evidence synthesis

Overall, 34 publications, mostly retrospective, fulfilled the main inclusion criterion. Main limitations of these studies were missing relevant information about design or analysis and heterogeneous methodology used. Inflammatory cells, costimulatory molecules in tumour cells, and serum cytokines showed prognostic significance, mainly in univariable analyses. High C-reactive protein values were consistently reported as an independent prognostic factor for mortality in invasive UBC.


There is a dearth of studies on InfBMs in UBC compared with other tumour types. Evidence suggests that InfBMs may have an impact on the management of patients with UBC. Currently, methodological drawbacks of the studies limit the translational potential of results.

Patient summary

In this review, we analysed studies evaluating the impact of inflammatory response on bladder cancer progression. Despite methodological limitations, some inflammatory biomarkers should be further analysed because they hold promise to improve patient care.

Take Home Message

Inflammatory biomarkers show promising utility in the management of bladder cancer patients. Evidence suggests that inflammatory biomarkers help predict bladder cancer prognosis; however, methodological drawbacks limit the applicability of results. Formal efforts should be made to follow proper methodological processes.

Keywords: Bladder cancer, Inflammation, Biomarkers, Progression, Survival.

1. Introduction

Evading the immune system is one of the emerging hallmarks of cancer [1] . It is well established that the inflammatory microenvironment has an impact on tumour prognosis, either positively or negatively [2] . The proper assessment of the composition and function of the microenvironment is challenging, and consensus is needed in the field about how to best consider the inflammatory response as a component of tumour subclassification [3] . In this regard, melanoma, colon, and breast cancer have taken the lead[4], [5], and [6].

The definition of inflammatory biomarkers (InfBMs) is in itself challenging. Any molecule involved in innate or adaptive immune response could be considered; this makes the list of candidates very long, and it is difficult to establish a definition of InfBMs due to the interaction between inflammatory pathways and other cellular functions. In this review, we focus on markers with the primary known function in the immune response.

Urothelial bladder carcinoma (UBC) is highly prevalent. It represents an important economic burden, affects patient quality of life, and is life threatening when it invades muscle. However, in many ways, UBC remains a neglected disease [7] . The dearth of information on InfBMs and UBC is paradoxical, considering that UBC is one of the few tumours for which there is long-standing evidence of the efficacy of immunotherapy.

Studies on the prognostic value of InfBMs in UBC have been published since the 1970s[8], [9], and [10]. The infiltration of the tumour by inflammatory cells and their association with prognosis has been explored more extensively than blood or urine cytokine levels and germline DNA polymorphisms in inflammatory genes. Unfortunately, none of these markers has proven to be sufficiently useful for clinical application. Methodological flaws, technical heterogeneity, and lack of appropriately designed validation studies have been the most important limitations. Guidelines were published in 2005 to improve the reporting of prognostic markers, but unfortunately they are rarely followed [11] . We report a systematic review of the published results and methods applied in studies that assessed tumour, blood, urine, and germline DNA InfBMs related to the prognosis of patients with UBC. The review was conducted following the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines criteria. The ultimate goals of this effort were to provide a rationale for promoting research in the promising field of immunity and UBC, to identify the main limitations of the studies performed, and to select the most promising markers for prospective studies and clinical trials through an integrative scope ( Fig. 1 ).


Fig. 1 Integrative scope of inflammatory biomarkers to support urothelial bladder cancer patient prognostication and treatment response prediction.

2. Evidence acquisition

2.1. Material and methods

2.1.1. Information sources and eligibility criteria

The Medline, Medline In-Process, and Embase databases were searched for all original articles published from January 1975 to November 2013 on the topic of interest. Medline was searched through PubMed. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines.

The inclusion criteria were (1) original article, (2) human research, (3) English language, (4) accessibility to the full manuscript, and (5) availability of Kaplan-Meier/Cox regression-derived results about the prognostic value of the InfBMs on UBC outcomes according to the REMARKS guidelines for assessment of a prognostic marker [12] . Studies using association tests instead of survival analysis, with or without adjustment for other relevant variables, were not included, but they are listed in Supplementary Table 1. The outcomes considered were recurrence and progression for non–muscle-invasive bladder cancer (NMIBC) and local progression, metastasis, and cancer-specific mortality and overall mortality for NMIBC and muscle-invasive bladder cancer (MIBC). We report on studies assessing an InfBM twice or more in the literature. All other studies are listed in Supplementary Table 1.

2.1.2. Search strategy

We searched PubMed using the controlled vocabulary of the Medical Subject Heading database along with open text. The algorithm applied was (bladder OR urothelium OR transitional cell) AND (cancer OR tumour OR tumour OR carcinoma OR neoplasm) AND (inflammation OR inflammatory OR immune OR immunity) AND (prognosis OR survival OR recurrence OR progression). The search in Embase used the Emtree vocabulary ‘bladder cancer’ AND (‘inflammation’ OR ‘immunity’) AND ‘prognosis’. All selected articles were further hand-searched to identify additional relevant articles.

2.1.3. Study selection and the data collection process

The first stage of the search in Medline and Embase was performed by A.M.L. to screen and exclude studies unrelated to UBC or InfBMs. Second-stage selection was performed by four investigators (A.M.L., Y.A., F.X.R., and N.M.).

Information was retrieved according to the REMARK guidelines for reporting prognostic markers including author, country, journal, publication year, marker examined, study design, study population (sample size, recruitment period, and follow-up), patient characteristics (age, gender, stage, comorbidities), treatment received, biologic material/matrix used (urine, tumour, serum, saliva), methodology (for urine and blood markers: dosage kit for the marker, period of retrieval, and cut-off for positivity; for immunohistochemistry (IHC) on tumour tissue: antibody, area of interest on the slide, magnification, scoring, cut-off for positivity, and percentage of positive tumours; for germline DNA variants/single-nucleotide polymorphism (SNP): gene name, and genotyping technique), statistical method applied (with variables used for adjustment), and reported impact of examined markers on UCB outcome using univariable or multivariable survival analyses.

2.1.4. Meta-analysis

We conducted a meta-analysis to summarise quantitatively the overall prognostic value of serum C-reactive protein (CRP) because this was the most frequently studied marker in association with UBC outcome (eight studies). One author (Pr. Saito [24]) was directly contacted to obtain data required for the meta-analysis. Two studies could not be included due to the lack of important information; the results of Ishioka et al. [22] could not be included because the variable was not log-transformed. Finally, we were obliged to stratify the meta-analysis according to whether the studies used a dichotomous variable (two studies) or a continuous variable (three studies) to assess CRP. Random effect meta-analysis was performed as a sensitivity analysis. We quantified heterogeneity using the I2statistic [12] that describes the proportion of heterogeneity across studies that is not due to chance. The analysis showed no heterogeneity between the studies (I2 = 0% for both type of studies). Consequently, a fixed effect model was applied. Risk of publication/reporting bias across studies is likely, although we could not test it appropriately because of the small number of studies included [13] . Analyses were done using R v.3.0.1 software.

3. Evidence synthesis

A total of 1045 original articles were identified using Medline and Medline In-Process and 1651 using Embase. Figure 2 shows a flow diagram of the study selection strategy. At the end of the process, there were 87 articles assessing the association between germline DNA, blood, urinary, or tumour InfBMs, and UBC outcome (77 from the online search abstract screening and 10 added from reference list screening). From those, 23 articles lacked a survival analysis, as defined in the REMARKS criteria, and 24 investigated a marker that had only been reported once in the UBC domain (see Supplementary Table 1). Six studies evaluated the association of cyclooxygenase-2 tumour expression and prognosis. Our group recently published a meta-analysis on this topic [14] ; therefore, these studies were not considered in this review. Finally, 34 original articles assessing 13 InfBMs (germline DNA variants in interleukin [IL]-6 and tumour necrosis factor [TNF]-α; serum CRP, IL-6, and CD8 levels, and neutrophil-to-lymphocyte ratio (NLR); and expression of CD3, CD8, CD68, CD83, FOXP3, B7-H1 (PD-L1), HLA class I molecules, HSP70, as well as “inflammatory infiltrate” in tumour samples) were included. None of the urinary InfBMs fulfilled the main study criteria.


Fig. 2 Flow diagram showing the study selection process. BCG = bacillus Calmette-Guérin; COX = cyclooxygenase; IL = interleukin.

3.1. Study methodology

There were 8 and 18 prospective and retrospective studies, respectively. This information was missing in eight studies. Only one article provided a rationale for sample size and statistical power. Studies included in the main tables were published between 1990 and 2013. Patient recruitment period ranged from 1971 to 2010. Median number of patients included was 69 (range: 30–530). Median age of the patients was 67 yr (range: 23–93 yr). Median follow-up was 29 mo (range: 1–240 mo) ( Table 1 ).

Table 1 Study and patients characteristics of the 34 studies

Study Marker Country No. of patients NMIBC/MIBC BCG Recruitment period Age, yr (range) Male/female, no. Follow-up (range) Treatment
Ahirwar et al. [15] IL-6 (rs1800795) India 136 136/0 69 2004–2007 62 (NA) 119/17 13 (3–60) TURB ± iBCG
Leibovici et al. [16] IL-6 (rs1800795) USA 353 204/149 123 1995–2003 NA NA 21 (1–74) TURB ± iBCG or mBCG or RC
Leibovici et al. [16] TNF-α (rs1800629) USA 465 204/146 123 1995–2003 NA NA 21 (1–74) TURB ± iBCG or mBCG or RC
Ahirwar et al. [17] TNF-α (rs1799964) India 73 73/0 73 2003–2007 61 (NA) NA 14 (3–60) TURB and iBCG
Hwang et al. [21] CRP Japan 67 0/67 0 2004–2010 71(45–86) 53/14 11 (2.5–46.5) Chemotherapy †
Yoshida et al. [25] CRP Japan 88 0/88 0 1997–2006 70 (63–75) 63/25 33 (3–117) Radiochemotherapy
Hilmy et al. [20] CRP UK 103 61/42 * 6 1992–2001 NA 70/33 60 (NA) NA
Gakis et al. [18] CRP Germany 246 0/246 26 1999–2009 67 (43–84) 191/55 30 (6–116) RC
Ishioka et al. [22] CRP Japan 232 0/232 NA 1995–2010 71 (66–77) 162/70 11 (NA) Radiochemotherapy or BSC
Nakagawa et al. [23] CRP Japan 114 NA 16 1990–2010 67 (32–84) 92/22 11 (0.2–206) RC
Saito et al. [24] CRP Japan 80 NA NA 2000–2009 NA 57/23 12 (2–99) Second-line chemo after RC
Gondo et al. [19] CRP Japan 189 62/127 NA 2000–2009 68 (38–85) 158/31 25 (2–128) RC
Gondo et al. [19] NLR Japan 189 62/127 NA 2000–2009 68 (38–85) 158/31 25 (2–128) RC
Krane et al. [27] NLR USA 68 NA NA 2005–2011 67.4 (NA) 55/13 25 (13–61) RC
Andrews et al. [66] IL-6 USA 51 15/36 24 1995–2000 65 (41–76) 47/4 46 (4–61) RC
Lin et al. [26] CD8 Taiwan 68 NA NA 2007–2008 67 (26–90) NA NA TURB
Otto et al. [67] CD3 Germany 61 61/0 NA 1995–1998 NA NA 74 (11–179) TURB
Winerdal et al. [29] CD3 Sweden 37 4/33 NA 1999–2002 67 (46–81) NA NA RC
Sharma et al. [28] CD8 USA 69 38/31 NA 1996–2001 NA 51/18 NA TURB or RC
Kitamura et al. [41] CD8 Japan 30 30/0 30 NA NA NA NA TURB and iBCG
Hanada et al. [39] CD68 Japan 63 40/23 NA NA 65 (34–84) 51/12 65 (3–153) TURB or RC
Kitamura et al. [41] CD68 Japan 30 30/0 30 NA NA NA NA TURB and iBCG
Takayama et al. [38] CD68 Japan 41 41 CIS/0 41 1995–2005 70 (47–91) 36/5 53 (3–120) TURB and iBCG
Ayari et al. [36] CD68 Canada 46 46/0 46 1997–2002 68 (NA) NA 31 (NA) TURB and mBCG
Ayari et al. [37] CD68 Canada 93 93/0 8 1990–1992 NA NA 66 (NA) TURB
Ayari et al. [36] CD83 Canada 53 53/0 53 1997–2002 68 (NA) NA 31 (NA) TURB and mBCG
Ayari et al. [37] CD83 Canada 93 93/0 8 1990–1992 NA NA 66 (NA) TURB
Winerdal et al. [29] FOXP3 Sweden 37 4/33 NA 1999–2002 67 (46–81) NA NA RC
Kitamura et al. [41] FOXP3 Japan 30 30/0 30 NA NA NA NA TURB and iBCG
Wang et al. [44] B7-H1 Japan 50 19/31 NA 2000–2002 62 (42–78) 40/10 28 (6–52) NA
Boorjian et al. [42] B7-H1 USA 316 98/218 NA 1990–1994 69 (37–90) 256/60 NA RC
Nakanishi et al. [43] B7-H1 Japan 65 36/39 NA 1996–2005 NA 47/18 26 (1–118) TURB or RC
Sharma et al. [28] HLA class 1 USA 69 38/31 NA 1996–2001 NA 51/18 NA TURB or RC
Levin et al. [45] HLA class 1 Israel 33 0/33 NA NA NA NA NA RC
Kitamura et al. [41] HLA class 1 Japan 30 30/0 30 NA NA NA NA TURB and iBCG
Homma et al. [46] HLA class 1 Japan 65 0/65 NA 1991–2002 65 (38–79) 51/14 35 (3–172) RC
Yu et al. [49] HSP70 Taiwan 530 530/0 NA 1991–2005 72 (23–92) 452/78 86 (1–240) TURB
Syrigos et al. [48] HSP70 Greece 67 15/52 NA NA NA NA NA NA
Cai et al. [30] Infl. infiltrate Italy 410 321/98 201 Jan–Dec 1995 68 (32–93) 306/104 NA TURB or RC
Samaratunga et al. [33] Infl. infiltrate Australia 60 NA 29 NA 72 (28–91) 51/9 57 (5–168) TURB
Offersen et al. [32] Infl. infiltrate Denmark 107 16/91 8 1992–1998 NA 91/16 NA TURB
Sanchez et al. [34] Infl. infiltrate Spain 194 194/0 NA 1976–1990 62 (18–93) 174/20 NA TURB ± instillations
Flamm and Havekec [31] Infl. infiltrate Austria 345 345/0 NA 1971–1982 69 (NA) 235/110 NA TURB ± instillations

* T1G3 tumours were considered MIBC.

Series of advanced urothelial bladder cancer.

Series of recurrence after RC.

± = with or without; BSC = best supportive care; CIS = carcinoma in situ; CRP = C-reactive protein; iBCG = induction bacillus Calmette-Guérin; IL = interleukin; Infl. = inflammatory; Jan–Dec = January through December; mBCG = maintenance bacillus Calmette-Guérin; MIBC = muscle-invasive bladder cancer; NA = not available; NLR = neutrophil-to-lymphocyte ratio; NMIBC = non–muscle-invasive bladder cancer; RC = radical cystectomy; TNF = tumour necrosis factor; TURB = transurethral resection of the bladder.

Missing information about study or patient characteristics was common: 11 (33%), 10 (30%), 5 (15%), 10 (30%), and 28 (84%) studies lacked information on age, gender, stage, follow-up, and comorbidities, respectively. Treatment information was provided by 31 studies (91%). When assessing germline DNA variants, all studies included NMIBC patients treated with bacillus Calmette-Guérin (BCG). Two studies also included a second cohort of cystectomised patients. For blood biomarkers, CRP was mainly investigated in patients undergoing radical cystectomy (RC) or among those with locally advanced/recurrent UBC treated by chemotherapy. NMIBCs were considered together with MIBC in two studies. Note that, in the study by Himly et al., pT1G3 patients were considered as having muscle-invasive UBC. For tumour markers, cohorts included both NMIBC and MIBC, separately or merged ( Table 1 ).

3.2. Assay methodology

The three studies considering germline DNA biomarkers analysed candidate SNPs in blood-extracted DNA. Genotyping was performed using methods based on polymerase chain reaction methods. Regarding blood biomarkers (11 studies), the dosage technique/kit was specified in four of eight studies on CRP. The threshold chosen to define positivity of CRP ranged from 0.5 to 1 mg/dl. In all studies, CRP was assessed before treatment (transurethral resection of the bladder [TURB] for NIMBC; RC or chemotherapy for MIBC). Two of eight studies considered CRP on a continuous scale. One study assessed serum IL-6 and categorised the levels using a threshold of 4.80 pg/ml (median value). NLR was assessed in two studies; both applied the same threshold (2.5). One study assessed the percentage of blood CD8 lymphocytes using flow cytometry; no further specification was given about the technique or threshold chosen for the analysis ( Table 2 ).

Table 2 Threshold and time of blood retrieval for serum markers

Study Marker Threshold in analysis Assessment period
Hwang et al. [21] CRP 1 mg/dl 1 d before first chemotherapy cycle
Yoshida et al. [25] CRP 0.5 mg/dl Before radiochemotherapy
Hilmy et al. [20] CRP 1 mg/dl Before transurethral resection
Gakis et al. [18] CRP 0.5 mg/dl or continuous 1–3 d before radical cystectomy
Ishioka et al. [22] Log CRP Continuous, mg/l Before chemo/radiation or best supportive care
Nakagawa et al. [23] CRP 0.5 mg/dl At recurrence
Saito et al. [24] CRP Continuous, mg/l Before second-line treatment
Gondo et al. [19] CRP 0.5 mg/dl Before radical cystectomy
Gondo et al. [19] NLR 2.5 Before radical cystectomy
Krane et al. [27] NLR 2.5 Before radical cystectomy
Andrews et al. [66] IL-6 4.80 pg/ml Morning of radical cystectomy
Lin et al. [26] CD8 NA Immediately before surgery

CRP = C-reactive protein; IL = interleukin; NA = not applicable; NLR = neutrophil-to-lymphocyte ratio.

As for tumour InfBMs (20 studies), all markers were assessed by IHC. Overall, reporting of technique and interpretation of results were heterogeneous. Full sections and tissue microarrays were used in 12 and 2 studies, respectively. This information was missing in five studies. Only seven studies reported how the region and compartment (tumoural/stroma) of interest were selected. Definition of a high-power field varied from × 200 to × 1000 magnification. Quantification of staining was highly variable. In six studies, no information was provided about the method of quantification. Similarly, six studies lacked information regarding the cut-off applied in survival analysis or the reference value chosen for Cox regression analysis. Five studies reported “inflammatory infiltrate” as a marker without further characterisation. The definition of “strong inflammatory infiltrate” varied widely ( Table 3 ).

Table 3 Tumour markers methodology

Study Marker Ab FS/TMA Region selection Area of interest Magnification Quantification of staining Cut-off for positivity/Reference in survival analysis % of PM
Otto et al. [67] CD3 Rabbit mono. FS 10 HPF in high CD3 + area Tumour nest ×1000 No. of CD3+ cells ≤4 vs >4 CD3 + cells/HPF NA
Winerdal et al. [29] CD3 Mouse mono. FS Random selection of 3 HPF Tumour nest ×400 No. of CD3+ cells <3 vs≥3 CD3 + cells/field (mean) NA
Sharma et al. [28] CD8 Mouse mono. FS 3 fields of 0.0625 mm2 in the high CD8 + area Tumour nest NA No. of CD8+ cells <8 vs ≥8 CD8 + cells/field (mean) NA
Kitamura et al. [41] CD8 Mono. FS NA NA ×400 0–10 CD8+ cells/HPF =  − ; 10–50 =  + ; >50 =  +  +  NA NA
Hanada et al. [39] CD68 Mono. FS 3 areas with the highest CD68 density Tumour nest ×200 NA <67 vs ≥67 NA
Kitamura et al. [41] CD68 Mono. FS NA NA ×400 NA NA
Takayama et al. [38] CD68 Mono. FS 6 random fields of 0.0625 mm2 Stroma and TN ×400 No. of CD68+ cells in S and TN <4 vs ≥4 TN/<24 vs ≥24 S 61
Ayari et al. [36] CD68 Mono. FS NA Stroma and TN ×200 0 (no CD68+ cells),1 (1–5),2 (6–10),3 (>10) 0–1 vs 2–3 (mean of S and TN) 46
Ayari et al. [37] CD68 Mono. FS NA Stroma and TN ×200 Same as 2009 0–1 vs 2–3 (mean of S and TN) 78
Ayari et al. [36] CD83 Mono. FS NA Stroma and TN ×200 0 (no CD68+ cells),1 (1–5), 2 (6–10), 3 (>10) 0–1 vs 2–3 (mean of S and TN) 54
Ayari et al. [37] CD83 Mono. FS NA Stroma and TN ×200 Same as 2009 0–1 vs 2–3 (mean of S and TN) 29
Winerdal et al. [29] FOXP3 Mono. mouse FS Random selection of 3 HPF Tumour nest ×400 No. of FOXP3 + cells <3 vs ≥3 FOXP3 + cells/field NA
Kitamura et al. [41] FOXP3 Mono. FS NA NA ×400 Same as CD8 and CD68 NA NA
Wang et al. [44] B7-H1 Poly. TMA NA NA ×400 % positive cells >10% NA
Boorjian et al. [42] B7-H1 NA FS NA NA NA % positive cells >5% 12
Nakanishi et al. [43] B7-H1 Mouse mono. FS Random selection of 3 HPF in high B7H1 + area Tumour nest ×400 % positive cells NA NA
Sharma et al. [28] HLA 1 Mono. NA NA NA NA % positive cells 10% NA
Levin et al. [45] HLA 1 Rabbit FS NA NA NA % positive cells 20% 57
Kitamura et al. [41] HLA 1 Mono. FS NA NA ×400 0,1–2 (low mb/cytoplasm), 0, 1, 2 vs 3 NA
              3 (mb and cytoplasm >80% cells)    
Homma et al. [46] HLA 1 Mono. FS 5 areas; no more precision Tumour cells ×400 0–1 = no/incomplete mb/cytoplasm staining, 2 = >80% mb staining 0–1 vs 2 66.2
Yu et al. [49] HSP70 NA TMA NA NA NA Intensity (0–3) × % stained cells NA NA
Sirigos et al. [48] HSP70 Rabbit poly. FS NA NA NA Intensity (0–3) × % stained cells 3 × 25% 66
Cai et al. [30] Infl. inf NA NA NA NA ×400 NA >20 lymphocytes 29
Samaratunga et al. [33] Infl. inf NA NA NA NA NA NA Inflammation vs oedema 36
Offersen et al. [32] Infl. inf NA NA NA NA ×200 NA Stroma not visible 30
Sanchez et al. [34] Infl. inf NA NA NA NA NA NA NA NA
Flamm and Havekec [31] Infl. inf NA NA Selection of 10 HPF NA NA NA >100 cells/HPF NA

Ab = antibody; mb = membrane; FS = full section; HPF = high-power field; Infl. inf = inflammatory infiltrate; mono. = monoclonal; NA = not available; PM = positive marker; poly. = polyclonal; S = stroma; TMA = tumour microarray; TN = tumour nest.

3.3. Study findings

InfBMs identified from the literature could be grouped in three large categories according to their biologic significance: inflammatory cells, inflammatory costimulatory molecules in tumour cells, and serum cytokines. Table 4 provides a detailed list of the biomarkers according to these three categories.

Table 4 Categories of inflammatory biomarkers considered in the review

Inflammatory cells Costimulatory molecules in tumour cells Serum cytokines
CD3 (cytotoxic TL) PDL1 CRP
CD8 (cytotoxic TL) HLA IL-6
FOXP3 (regulatory TL) HSP TNF-α
CD68 (macrophages)    
CD83 (dendritic cells)    
Inflammatory infiltrate    
Neutrophil-to-lymphocyte ratio    

CRP = C-reactive protein; IL = interleukin; TL = T lymphocyte; TNF = tumour necrosis factor.

3.3.1. Germline DNA inflammatory biomarkers

Two studies assessed the association between germline IL-6 rs1800795 variant and UBC outcome[15] and [16]( Table 5 ). Ahirwar et al. found a decreased risk of NMIBC recurrence among carriers (hazard ratio [HR]: 0.41; 95% confidence interval [CI], 0.17–0.94) [15] ; Leibovici et al. described a 4.6-fold higher risk of recurrence among high-risk NMIBC carriers receiving maintenance BCG therapy (n = 38) [16] . As for TNF-α, two polymorphisms were studied (rs1800629 and rs1799964)[16] and [17]. Both studies found that the SNP considered was associated with a decreased risk of recurrence among patients with NMIBC. However, the findings among patients with MIBC were discordant.

Table 5 Impact of high levels of germline variant and serum markers on bladder cancer survival

Study Marker Outcome Log rank p value Cox regression Adjusted for Note
        Univariate p value Multivariate HR (95% CI) p value    
Ahirwar et al. [15] IL-6 Recurrence     0.41 (0.17–0.94) 0.03 Age, gender All NMIBC
Leibovici et al. [16] IL-6 Recurrence     0.73 (0.38–1.39) NS Age, gender, smoking status, grade iBCG
  Recurrence     4.6 (1.24 -17.1) NA Age, gender, smoking status, grade mBCG
  Progression     0.88 (0.80–4.41) NA Age, gender, smoking status, grade All NMIBC
  CSS 0.018   0.39 (0.15–1.00) NA Age, gender, smoking status, grade Only for MIBC
  OS     0.43 (0.19–0.94) NA Age, gender, smoking status, grade Only for MIBC
Leibovici et al. [16] TNF-α Recurrence     1.69 (0.86–3.32) NA Age, gender, smoking status, grade iBCG
  Recurrence     0.43 (0.12–0.49) NA Age, gender, smoking status, grade mBCG
  Progression     0.71 (0.27–1.85) NA Age, gender, smoking status, grade All NMIBC
  CSS     1.54 (0.58–4.12) NA Age, gender, smoking status, grade Only for MIBC
  OS     2.35 (1.07–5.16) NA Age, gender, smoking status, grade Only for MIBC
Ahirwar et al. [17] TNF-α Recurrence 0.024   0.38 (0.14–0.98) 0.048 Age, gender, smoking status iBCG
Hwang et al. [21] CRP OS 0.001 0.001 NS NS Age, gender, metastasis by location, albumin level, ECOG  
Yoshida et al. [25] CRP CSS 0.0003 0.003 1.80 (1.01–2.97) 0.046 stage  
Himly et al. [20] CRP CSS NA 0.016 2.89 (1.42–5.91) 0.004 Ki-67/COX2 expression, adjuvant therapy Stratified by stage
Gakis et al. [18] CRP CSS <0.001 0.0012 1.18 (1.09–1.27) <0.001 Stage, LN density, margins CRP = continuous variable
Ishioka et al. [22] Log CRP OS <0.0001 <0.001 1.6 (1.19–2.15) <0.01 Age, gender, PS, Hb, LDH, visceral metastasis * , LN metastasis  
Nakagawa et al. [23] CRP OS <0.0001 <0.0001 2.62 (1.6–4.4) 0.0002 Time to recurrence, symptoms at recurrence, no. of metastatic organs, LDH, chemo, metastasectomy  
Saito et al. [24] CRP OS NA <0.01 1.02 (1.01–1.02) 0.001 ECOG, number of metastatic sites and nadir CRP after treatment CRP = continuous variable
Gondo et al. [19] CRP CSS 0.025 0.02 NA NA NA  
Gondo et al. [19] NLR DSS 0.0015 0.0015 1.94 (1.03–3.66) 0.038 Tumour size, hydronephrosis, hemoglobin  
Krane et al. [27] NLR OS 0.04 NA 2.49 (1.14–6.09) NA pT, pN, albuminemia, creatininemia, refraction to BCG  
Andrews et al. [66] IL-6 MFS 0.024 NA 1.41 (0.95–2.10) 0.08 Stage, grade, LVI, nodal status  
CSS 0.015 NA 2.17 (1.29–3.65) 0.05 Stage, grade, LVI, nodal status  
Lin et al. [26] CD8 Recurrence 0.018 0.044 0.40 (0.17–0.94) 0.036 NA Ref. = high CD8

* Liver, lung, and bone.

BCG = bacillus Calmette-Guérin; CI = confidence interval; COX = cyclooxygenase; CRP = C-reactive protein; CSS = cancer-specific survival; ECOG = Eastern Cooperative Oncology Group; Hb = hemoglobin; HR = hazard ratio; iBCG = induction bacillus Calmette-Guérin; IL = interleukin; LDH = lactate dehydrogenase; LN = lymph node; LVI = lymphovascular invasion; BCG = maintenance bacillus Calmette-Guérin; MFS = metastasis-free survival; NA = not available; NLR = neutrophil-to-lymphocyte ratio; NMIBC = non–muscle-invasive bladder cancer; NS = nonsignificant; OS = overall survival; PS = performance status; TNF = tumor necrosis factor.

3.3.2. Blood inflammatory biomarkers

CRP was the most widely studied serum marker. Eight studies assessed the association of high CRP levels with overall or cancer-specific survival[18], [19], [20], [21], [22], [23], [24], and [25]. All of them reported consistent results showing that high CRP levels are associated with adverse outcome. In one study, the association was only significant in univariable analysis [21] . Another study lacked multivariable analysis [19] . In the six remaining studies, three using dichotomised CRP levels and three using a continuous variable, CRP was an independent prognostic factor for both cancer-specific and overall mortality, although variables for adjustment varied among studies ( Table 5 ; Supplementary Fig. 1 and 2). CD8 cell count was assessed as a serum marker using flow cytometry [26] . Authors observed that serum CD8 was inversely correlated to tumour infiltration with CD8 cells (r2 = 0.63;p < 0.0001). Low levels of CD8 cells in blood were associated with lower intravesical recurrence after TURB applying a multivariable analysis (HR: 0.4; 95% CI, 0.17–0.94). Two studies reported prognostic value for NLR after RC for UCB[19] and [27]. With a threshold of 2.5, both studies observed that high NLR was an independent adverse prognostic factor for survival after RC (HR: 1.94; 95% CI, 1.03–3.66, and HR: 2.49; 95% CI, 1.14–6.09). Tumour inflammatory biomarkers: inflammatory cells in the tumour environment

Tumour infiltration by CD3+and CD8+cells was associated with a better outcome ( Table 6 ). All studies showed significant results in univariable analysis. Results were confirmed after adjustment for stage and other prognosticators only in the two studies assessing MIBC[28] and [29]. Winerdal et al. considered the impact of CD3 lymphocyte infiltration in the tumour of 37 patients treated by RC [29] . Strong infiltration was associated with increased overall survival (HR: 0.24; 95% CI, 0.08–0.71;p = 0.01). Sharma et al. studied CD8 infiltration in a series of 69 patients treated with TURB or RC [28] . HR for overall survival among MIBC patients with strong CD8 cell infiltration in the tumour on RC specimen was 0.3 (95% CI, 0.09–0.96). The presence of an “inflammatory infiltrate” without further characterisation of the cells has also been widely studied[30], [31], [32], [33], and [34]. The main limitations of those studies were the heterogeneity in the definition of the studied populations and the terminflammatory infiltrate. Three studies including NMIBC and MIBC patients showed that strong inflammatory infiltrate was an independent good prognosticator for survival after adjustment for stage and other clinicopathologic factors[30], [32], and [35]. Despite some caveats, the available evidence supports the notion that strong inflammatory infiltration in the tumour might be associated with a better prognosis, in concordance with the studies of CD3 and CD8. A precise characterisation of the inflammatory infiltrate in the tumour is required to draw further conclusions and establish mechanistic hypotheses.

Table 6 Impact of tumour markers overexpression on bladder cancer survival

Study Marker Outcome Log rank p value Cox regression Adjusted for Notes
        Univariate p value Multivariate HR (95% CI) p value    
Otto et al. 2012 CD3 CSS 0.045 NA 0.40 (0.05–3.24) 0.39 Age, gender, grade, tumour size, CIS, BCG, early or deferred cystectomy  
Winerdal et al. [29] CD3 OS 0.04 0.013 0.24 (0.08–0.71) 0.01 Age, gender, T stage, metastasis, chemotherapy, FOXP3 expression  
Sharma et al. [28] CD8 OS 0.001 0.15 0.3 (0.09–0.96) 0.04 Stage Only for MIBC
Kitamura et al. [41] CD8 Recurrence 0.0001 0.33 0.89 (0.12–6.59) 0.9 Stage, grade, HLA, CD4, CD20, CD68, TIA1, S100, FOXP3  
Hanada et al. [39] CD68 Survival <0.0001 0.0005 5 (1.9–12.6) 0.0005 Age, grade, microvessel count, LVI, distant metastasis  
Kitamura et al. [41] CD68 Recurrence 0.0039 0.32 0.31 (0.03–2.86) 0.29 Stage, grade, HLA, CD4, CD20, CD68, TIA1, S100, FOXP3  
Takayama et al. [38] CD68 T Recurrence 0.0002 NA 1.73 (1.47–5.03) 0.0012 Age, gender T = in CIS
CD68 S Recurrence 0.77 NA 1.01 (0.91–1.09) 0.87 Age, gender S = lamina propria
Ayari et al. [36] CD68 Recurrence 0.093 0.101 3.81 (1.32–11) 0.013 Age (continuous), gender, T stage, number of BCG maintenance  
Ayari et al. [37] CD68 Recurrence NA NA 1.19 (0.5–2.5) 0.65 Age (continuous), T stage, grade, number of tumours Stratified by sex and SS
  Progression 0.09 NA NA NA Age (continuous), T stage, grade, number of tumours Stratified by sex and SS
Ayari et al. [36] CD83 Recurrence NA 0.045 9.81 (1.1–85.7) 0.039 Age (continuous), gender, T stage Only if >1 mBCG
Ayari et al. [37] CD83 Recurrence NA NA 0.93 (0.5–1.8) 0.84 Age (continuous), T stage, grade, no. of tumours Stratified by sex and SS
  Progression 0.04 NA 8.25 (1.4–47.3) 0.018 Age (continuous), T stage, grade, no. of tumours Stratified by sex and SS
Winerdal et al. [29] FOXP3 OS 0.037 0.019 0.17 (0.05–0.6) 0.006 Age, gender, T stage, metastasis, chemotherapy, CD3 expression  
Kitamura et al. [41] FOXP3 Recurrence NA 0.11 0.19 (0.02–1.41) 0.106 Stage, grade, HLA, CD4, CD20, CD68, TIA1, S100, FOXP3  
Wang et al. [44] B7-H1 CSS 0.02 NA 2.24 (1.16–4.38) 0.01 Age, gender, stage, grade  
Boorjian et al. [42] B7-H1 OS 0.005 0.005 3.18 (1.74–5.79) <0.001 Age, stage, ECOG, smoking  
Nakanishi et al. [43] B7-H1 OS 0.021 NA NA NA   OE = worse survival
Sharma et al. [28] HLA 1 MFS NA 0.09* NA NA   * (HR: 0.57)
Levin et al. [45] HLA 1 CSS <0.005 NA NA NA   OE = better CSS
Kitamura et al. [41] HLA 1 Recurrence 0.019 0.04 0.06 (0.01–0.40) 0.003 Stage, grade, HLA, CD4, CD20, CD68, TIA1, S100, FOXP3 Ref. = HLA low
Homma et al. [46] HLA 1 Rec. after RC 0.03 0.03 2.39 (1.2–4.8) 0.01 pT, pN, and histologic variant Ref. = HLA high
Yu et al. [49] HSP70 Recurrence NA 0.001 1.52 (1.15–2) <0.001 Multiplicity OE = more recurrence
Sirigos et al. [48] HSP70 OS <0.05 NA NA NS Stage and grade OE = poor survival
Cai et al. [30] Infl. Inf OS 0.0098 NA NA 0.027 Stage and grade OE = better survival
Samaratunga et al. [33] Infl. Inf MFS 0.56 NA NA NS Age, gender, grade, size, treatments, multifocality OE = better survival
Offersen et al. [32] Infl. Inf CSS 0.004 NA 0.48 (0.24–0.96) 0.04 Stage, nodal status, grade, and vessel density  
Sanchez et al. [34] Infl. Inf CSS <0.01 NA NA NS Grade OE = poor survival
Flamm and Havekec [31] Infl. Inf CSS 0.053 NA NA 0.021 Stage, grade, multiplicity, location, CIS, and adjuvant treatment OE = better survival

BCG = bacillus Calmette-Guérin; CI = confidence interval; CIS = carcinoma in situ; CSS = cancer-specific survival; ECOG = Eastern Cooperative Oncology Group; HR = hazard ratio; iBCG = induction bacillus Calmette-Guérin; Infl. inf = Inflammatory infiltrate; LVI = lymphovascular invasion; mBCG = maintenance bacillus Calmette-Guérin; MFS = metastasis-free survival; NA = not applicable; NMIBC = non–muscle-invasive bladder cancer; NS = not significant; OE = overexpression; OS = overall survival; RC = radical cystectomy; Rec = recurrence; S = count in stroma; SS = smoking status; T = count in tumour.

It is difficult to come to a conclusion on the value of CD68 because of the heterogeneity of patient characteristics, tissue location, and methods of quantification. Ayari et al. combined the count in the tumour and in the stroma, whereas Takayama et al. did it separately[36], [37], and [38]. A total of three studies showed statistically significant results in multivariate analysis, with strong infiltration by CD68 macrophages associated with adverse outcome[36], [38], and [39]. Ayari et al. described that CD68 infiltration in the tumour was associated with a 3.8-fold (95% CI, 1.32–11;p = 0.013) higher risk of recurrence after TURB and maintenance BCG in a series of patients with NMIBC [36] . This result was not validated by a subsequent publication of the same author in a series of patients not treated with BCG therapy [37] . Hanada et al. combined NMIBC and MIBC patients and showed that those presenting strong CD68 infiltration in the tumour had a fivefold higher risk of mortality [39] . However, the study lacked details about patient management and definition of survival end points. Finally, Takayama et al. published a study with 41 patients with carcinoma in situ (CIS) treated with TURB and induction BCG [38] . The end point was time to recurrence defined by positive cytology. In multivariate analysis adjusted for age and gender, high CD68 count in CIS regions (four or more CD68+cells) was independently associated with recurrence (HR: 1.7). These authors also analysed CD68 counts in the lamina propria but found no association with recurrence. Together with CD68, Ayari et al. explored the impact of dendritic cell infiltration defined by CD83, and high levels of infiltration were associated with adverse outcome—recurrence after maintenance BCG [36] —and progression to muscle invasion [39] . Results were statistically significant, but the reliability of the risk estimates by Cox was questionable because of the small number of events (only one tumour recurred in the low CD83 group yielding an upper 95% CI of 85). Altogether these papers pinpoint a possible adverse effect of macrophage infiltration in tumours, but evidence remains weak.

The transcription factor FOXP3 is a master regulator of regulatory T cells (Treg) [40] . Two studies analysed tumour infiltration by FOXP3+lymphocytes[29] and [41]. Both showed better survival in association with strong infiltration, although the findings were statistically significant, after adjustment, in only one of them (HR: 0.17; 95% CI, 0.05–0.6;p = 0.006, for overall survival) [29] . Tumour inflammatory biomarkers: costimulatory molecules in tumour cells

B7-H1 (PD-L1) is a T-cell coregulatory molecule. All three studies evaluating this marker showed that high B7-H1 expression was associated with decreased cancer-specific or overall survival (two studies after adjustment for classical prognosticators and one in univariable analysis only)[42], [43], and [44]. In a series of cystectomised patients, Boorjian et al. demonstrated that B7-H1 expression (>5% cells) was associated with a 3.18-fold higher risk of overall mortality compared with those lacking the marker (95% CI, 1.74–5.79;p < 0.001) [42] .

HLA class I molecules are required for the cytotoxic activity of T cells. Loss of HLA class 1 molecules was associated with adverse outcome. Four studies evaluated the impact of HLA class I expression on survival[28], [41], [45], and [46]. Strong HLA class I expression was associated with decreased recurrence in a series of patients with NMIBC treated with TURB and induction BCG (HR: 0.06; 95% CI, 0.01–0.40;p = 0.003) [41] . The main limitation of the study was the small sample size (n = 30 patients) and number of events. Homma et al. presented a series of 65 patients treated with RC with similar results [46] . Patients whose tumour lost HLA class I expression had a 2.39-fold higher risk of recurrence after RC than those who did not, after adjustment for stage and histologic variant. HSP70 participates in pro- or antitumour immunity through its secretion by tumour cells or membrane expression through which it can present antigens to the immune system [47] . HSP70 has been evaluated twice in UBC[48] and [49]. Yu et al. assessed its association with recurrence and progression in a series of 530 patients with NMIBC treated by TURB [49] . Strong expression of HSP70 in tumour cells was independently associated with an increased risk of recurrence (HR: 1.52; 95% CI, 1.15–2;p < 0.001), but there was no independent association with progression in a multivariable analysis. By contrast, HSP27 expression was independently inversely associated with progression (HR: 0.49; 95% CI, 0.33–0.73;p < 0.0001).

3.4. Discussion

In spite of their limitations, the major prognosticators for both NMIBC and MIBC at present remain clinical and pathologic factors. The usefulness of several promising molecular prognostic markers, such as Ki-67 overexpression or fibroblast growth factor receptor 3 (FGFR3) mutations, has not been conclusively established. Other markers, such as tumour protein p53 (TP53)mutations or p53 overexpression, have failed to demonstrate clinical usefulness when combined with standard clinical and pathologic parameters [50] . Molecular profiling has identified new subgroups of UBC, but independent replication is required[51] and [52]. In addition to their prognostic value, such profiles aim at improving patient stratification and outcome prediction through the use of targeted therapies. However, none of these strategies is ready to be used in the clinical setting.

Overall, there is a need to improve methodology to assess prognostic markers in UBC, as in other tumour types [53] . The field of InfBM discovery is no exception to that rule. The REMARKS guidelines, published in 2005, provide a quality control framework to improve research quality for prognosis biomarker assessment [11] . From the 34 studies analysed in this review in detail, 28 were published in or after 2005. Most of them did not fulfil the REMARKS criteria. Study and patient characteristics were often poorly described, assay methodology was very heterogeneous, important information was missing, and analysis and presentation of results were irregular. Unsuitable methodology prevents reproducibility and diminishes the impact of the work.

The lack of rigor in conducting and reporting studies renders validation of the results less likely. In this review, none of the studies included provided internal or external validation. Statistical significance from Cox multivariable analysis does not mean that a marker is worth translation into clinics. Studies should provide evidence of the marker's analytical validity (robustness of the test method) and discriminative ability (ie, c-index) [54] . Finally, the ideal biomarker should allow identification of patients at risk of a certain outcome with acceptable cost [4] . None of the studies included in this review provided any of those estimates. Consequently, translation into the clinics is inefficient, and much effort is wasted in the replication of poor quality studies. Despite these methodological limitations, we provide evidence that some InfBMs merit additional study as markers of UBC prognosis ( Fig. 3 ).


Fig. 3 Role of inflammatory biomarkers in the development and progression of urothelial bladder cancer. BCG = bacillus Calmette-Guérin; CRP = C-reactive protein; IL = interleukin; NLR = neutrophil-to-lymphocyte ratio; NSAID = nonsteroidal anti-inflammatory drug; UTI = urinary tract infection.

SNPs in inflammatory pathways possibly play a role in UBC prognosis, although their individual value will probably be too small to be useful in the clinical setting. It is, however, likely that the combined effect of multiple polymorphisms may be more important and more robust as a marker. The multi-inflammatory SNP approach has already been applied to UBC risk studies [55] , and similar approaches should be applied to prognosis.

The prognostic significance of IL-6 and CRP has been demonstrated in other cancers including lung, breast, ovary, colon, prostate, and upper urinary tract. Our review suggests that CRP may be the first InfBM approaching translation into clinics. All studies reviewed consistently provide evidence that patients with a high CRP level have a poorer prognosis in MIBC, independently of standard clinical or pathologic factors. However, these results need to be interpreted cautiously because of the heterogeneous methodology applied. The prognostic significance of CRP in patients with NMIBC is unknown and also merits study.

NLR has recently attracted interest and is a promising marker for patients undergoing radical surgery. Since we performed this search, two additional studies have shown that high NLR is associated with adverse outcome in UBC and in upper tract urothelial carcinoma[56] and [57]. A recent meta-analysis exploring the impact of NLR in all urologic malignancies confirmed its significance in urothelial carcinoma and showed comparable results for renal cell carcinoma [58] .

There is also an increasing interest in inflammatory infiltrates of tumours. Galon et al. reported that the type, location, and density of inflammatory infiltrating cells in colorectal carcinoma were better predictors of survival than the commonly used clinical and histopathologic factors [5] . An Immunoscore based on the assessment of CD3+and CD8+cells in the centre of the tumour and at the invasion front was established for clinical translation [59] , and a worldwide task force has been organised to “initiate the incorporation of Immunoscore as a component of cancer classification” [3] . We show that CD3 and CD8 infiltration in the tumour are directly associated with a better UBC prognosis, as in colon cancer. One of the main limitations, however, is the lack of standardised methods of immunostaining and scoring: Variation of the cut-off chosen for positivity and lack of normalisation of the results (ie, per square millimetre) reduces the robustness of the results. The relevance of these markers is also supported by gene expression profiling of UBC showing the existence of an “infiltrated” subtype characterised by a strong “immune signature.” Sjodahl et al. reported high levels of CD3d molecule, delta (CD3-TCR complex) (CD3D), CD3 g molecule, gamma (CD3-TCR complex) (CD3G), and CD8a molecule (CD8A) transcripts in UBC with preliminary data suggesting an association with a better prognosis [60] .

The interplay between inflammatory and tumour cells should also be assessed. PD-L1 is important as a marker and as a therapeutic target [61] . Interaction between PD-L1 and its receptor PD1 transmits an inhibitory signal to CD8+lymphocytes, reducing their proliferation [62] . The three studies reviewed in this paper showed that PD-L1 tumour cell expression was associated with adverse outcome. Anti–PD-L1 antibodies have shown remarkable antitumour activity and safety in recent clinical trials in patients with advanced cancers [63] . In melanoma, anti-PD1 and anti–PD-L1 antibodies induced high rates of durable responses [64] . Clinical trials are now being conducted in metastatic UBC.

To translate knowledge into the management of patients with UBC, several specific challenges need to be addressed: Tumours are highly heterogeneous; the TURB generally disrupts the morphology of the tumour mass, which is fragmented; the natural history of the disease is long and subject to medical intervention, for example with nonsteroidal anti-inflammatory drugs; and chronic cystitis, associated with lymphocytic and lymphofollicular infiltrates, can occur in the setting of UBC. Immune editing secondary to BCG immunotherapy may be particularly relevant. Regarding MIBC, it will be important to assess incident tumours separately from those that progress from NMIBC [65] . These issues will require well-designed prospective multicentre studies of appropriate sample size.

This review has some limitations. Reporting and publication bias may result from the lack of information or unpublished negative studies. The reduced number of studies for each marker did not allow us to provide a quantitative assessment of the presence of bias. In addition, the power of meta-analyses is limited due to the small number of studies included, making assessment of heterogeneity and the interpretation of results difficult. The search, using very general keywords, demonstrated problems in the referencing of inflammatory-related articles in Medline. A hand search of reference lists of retrieved publications was necessary to identify most of the studies regarding CRP. This also outlines the difficulties of defining an inflammatory marker. Undoubtedly, some markers are directly implicated in immune response, such as CD3, CD8, HLA, or CRP. Others, such as HSP70, have more pleiotropic functions. Finally, markers included in Supplementary Table 1 are not necessarily devoid of interest: They just require more attention. A major emphasis should be placed on building partnerships to conduct larger and better studies. The study of the tumour inflammatory microenvironment promises novel insight into cancer biology and raises new opportunities for therapeutic intervention. The urologic community should contribute so that progress in the management of patients with UBC does not get “lost in translation.”

4. Conclusions

InfBMs show promising usefulness in the management of patients with UBC, both for improved assessment of prognosis and to guide therapy. Serum CRP is one of the most promising InfBMs and independently associated with mortality in advanced UBC. However, these findings need to be interpreted with caution because of many methodological drawbacks. In the translational process of these markers into the clinical milieu, rigorous efforts should be placed in proper study design. Progress will be faster if researchers unite their efforts.

Author contributions:Núria Malats had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design:Masson-Lecomte, Allory, Malats.

Acquisition of data:Masson-Lecomte.

Analysis and interpretation of data:Masson-Lecomte, Rava, Real, Allory, Malats.

Drafting of the manuscript:Masson-Lecomte, Allory, Malats.

Critical revision of the manuscript for important intellectual content:Masson-Lecomte, Rava, Hartmann, Real, Allory, Malats.

Statistical analysis:Masson-Lecomte, Rava.

Obtaining funding:Masson-Lecomte, Allory, Malats.

Administrative, technical, or material support:None.

Supervision:Allory, Malats.

Other(specify): None.

Financial disclosures:Núria Malats certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None.

Funding/Support and role of the sponsor:This work was partially funded by a fellowship of the European Urological Scholarship Program for Research given to Alexandra Masson-Lecomte (EUSP Scholarship S-01-2013); Red Temática de Investigación Cooperativa en Cáncer (#RD12/0036/0050) for supporting the two groups at CNIO and Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III, Spain (Grant numbers #PI00-0745, #PI05-1436, and #PI06-1614); and EU-7FP-HEALTH-TransBioBC #601933 for all the background provided by these studies that is exploited here.

Appendix A. Supplementary data

Supplementary Fig. 1 Forest plot from the meta-analysis of the three studies on serum C-reactive protein (CRP), considered as a dichotomous variable, and overall survival or cancer-specific survival. The diamond indicates overall hazard ratio (HR) (meta-HR) and 95% confidence interval for mortality associated with values of CRP over the cut-off value.

Supplementary Fig. 2 Forest plot from the meta-analysis of the two studies on serum C-reactive protein (CRP), considered as a continuous variable, and overall survival or cancer-specific survival. The diamond indicates overall hazard ratio (HR) (meta-HR) and 95% confidence interval for mortality associated with per unit increase in CRP level. Note that for the work of Saito et al., HR was transformed because CRP was assessed using different units (milligrams per liter instead of milligrams per deciliter).



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a Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

b Urology Department, Henri Mondor Academic Hospital, INSERM U955Eq7, Créteil, France

c Epithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

d Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain

e Department of Pathology, University Erlangen-Nürnberg, Erlangen, Germany

f Pathology Department, Henri Mondor Academic Hospital, INSERM U955Eq7, Créteil, France

lowast Corresponding author. Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández Almagro, 3, 28029 Madrid, Spain. Tel. +34 912 246 900 ext. 3330; Fax: +34 912 246 911.