Interleukin-8 and Interleukin-6 are biomarkers of poor prognosis in Esophageal squamous cell carcinoma

Citation: Pastrez, P.R.A.; Barbosa, A.M.; Mariano, V.S.; Causin, R.L.; Castro, A.G.; Torrado, E.; Longatto-Filho, A. Interleukin-8 and Interleukin-6 Are Biomarkers of Poor Prognosis in Esophageal Squamous Cell Carcinoma. Cancers 2023,15, 1997. https://doi.org/10.3390/ cancers15071997 Academic Editors: William Cho and Anquan Shang Received: 2 January 2023 Revised: 3 February 2023 Accepted: 6 February 2023 Published: 27 March 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). cancers Article Interleukin-8 and Interleukin-6 Are Biomarkers of Poor Prognosis in Esophageal Squamous Cell Carcinoma Paula Roberta Aguiar Pastrez 1, Ana Margarida Barbosa 2,3, Vânia Sammartino Mariano 1,4, Rhafaela Lima Causin 1, Antonio Gil Castro 2,3, Egídio Torrado 2,3 and Adhemar Longatto-Filho 1,2,3,5,* 1 Teaching and Research Institute, and Molecular Oncology Research Center, Barretos Cancer Hospital—Pio XII Foundation, Barretos 14784-390, Brazil 2Life and Health Sciences Research Institute (ICVS), University of Minho, 4710-057 Braga, Portugal 3ICVS/3B’s—PT Government Associate Laboratory, 4806-909 Braga/Guimarães, Portugal 4Anhanguera Faculty in Piracicaba, Piracicaba 13416-257, Brazil 5Medical Laboratory of Medical Investigation (LIM) 14, Department of Pathology, Faculty of Medicine, University of São Paulo, São Carlos 13566-590, Brazil *Correspondence: [email protected]; Tel.: +351-253-604-8827 Simple Summary: Esophageal squamous cell carcinoma (SCC) is an extremely aggressive malignancy with high mortality rates. An important variable in understanding this aggressiveness seems to be in controlling the proliferation of malignant cells directly or indirectly related to systemic levels of inflammatory cytokines. Therefore, we sought to assess the usefulness of determining the most prominent cytokines in this context and their potential role as diagnostic biomarkers. For this purpose, we analyzed the levels of IL-1 β , IL-6, IL-8, IL-10, TNFα and IL-12p70 in a group of 70 patients with ESCC and 70 healthy individuals and detected increased levels of IL-1 β , IL-6, IL-8 and IL-10 in patients with ESCC compared to controls. We also observed that patients with low IL-6, IL-8 had a significantly higher overall survival rate. To confirm these findings, we studied Kyse-30 and Kyse-410 cells cultured in mice and confirmed that increased growth of these cells was associated with recruitment/accumulation of intratumoral polymorphonuclear leukocytes. Abstract: Esophageal squamous cell carcinoma (ESCC) is a common type of cancer characterized by fast progression and high mortality rates, which generally implies a poor prognosis at time of diagnosis. Intricate interaction networks of cytokines produced by resident and inflammatory cells in the tumor microenvironment play crucial roles in ESCC development and metastasis, thus influencing therapy efficiency. As such, cytokines are the most prominent targets for specific therapies and prognostic parameters to predict tumor progression and aggressiveness. In this work, we examined the association between ESCC progression and the systemic levels of inflammatory cytokines to determine their usefulness as diagnostic biomarkers. We analyzed the levels of IL-1 β , IL-6, IL-8, IL-10, TNFα e IL-12p70 in a group of 70 ESCC patients and 70 healthy individuals using Cytometric Bead Array (CBA) technology. We detected increased levels of IL-1 β , IL-6, IL-8, and IL-10 in ESCC patients compared to controls. However, multivariate analysis revealed that only IL8 was an independent prognostic factor for ESCC, as were the well-known risk factors: alcohol consumption, tobacco usage, and exposure to pesticides/insecticides. Importantly, patients with low IL-6, IL-8, TNM I/II, or those who underwent surgery had a significantly higher overall survival rate. We also studied cultured Kyse-30 and Kyse-410 cells in mice. We determined that the ESCC cell line Kyse-30 grew more aggressively than the Kyse-410 cell line. This enhanced growth was associated with the recruitment/accumulation of intratumoral polymorphonuclear leukocytes. In conclusion, our data suggest IL-8 as a valuable prognostic factor with potential as a biomarker for ESCC. Keywords: cytokines; tumor microenvironment; esophageal cancer; inflammation; Interleukin-8; Interleukin-6 Cancers 2023,15, 1997. https://doi.org/10.3390/cancers15071997 https://www.mdpi.com/journal/cancers Cancers 2023,15, 1997 2 of 16 1. Introduction Esophageal cancer (EC) is the eighth most common cancer worldwide which has the sixth worst prognosis, with a 5-year survival rate of 15–25% [ 1 – 4 ]. The two main histological types, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma, have been spreading globally over the last three decades with some particular variations among them [ 5 ]. Indeed, while there has been a recent increase in the absolute numbers of esophageal adenocarcinomas in Western industrialized countries [ 6 , 7 ], the number of cases of ESCC has shown a small reduction in regions of high risk [ 7 ]. Despite this decrease, ESCC is still the most common type of EC globally, being highly prevalent in Asia while its incidence has remained relatively constant in the USA and Western Europe [7]. The development of ESCC depends on a wide variety of etiological factors that may or may not act concomitantly. Environmental factors and genetic predisposition with well-established risk factors such as alcohol intake, cigarette smoking and malnutrition play a decisive role in the progression and aggressiveness of this type of cancer [ 4 , 8 ]. More recently, obesity and chronic inflammation have also emerged as key factors in ESCC development and progression [ 9 ]. In this regard, several pro-inflammatory cytokines and growth factors have been identified as critical factors in ESCC progression, as their expression correlated with the clinical, pathological and survival rates of ESCC patients [ 9 ]. Therefore, while the mechanisms whereby cytokines modulate the anti-tumor immunity are not fully understood, these studies suggest that cytokines may be useful for diagnosis (detected when disease is present), prognosis (associated with disease outcome), or as predictive biomarkers (associated with drug response) of ESCC [ 9 ]. Among cytokines tested, the levels of Interleukin (IL)-1 β , IL-6 and IL-8 have been associated with ESCC progression and metastasis, whereas the levels of IL-2, Interferon-gamma (IFNγ ), IL-12, and IL-18 appear to stimulate antitumor immune responses [ 9 ]. These data show that while on one hand, the cytokine network is crucial to potentiate anti-tumor immunity, on the other hand, it can also induce immune dysfunctions and contribute to tumor progression and metastasis [ 9 , 10 ]. Accordingly, ESCC pathogenesis has been associated with the deregulation of many cytokines and chemokines that are involved in metastasis and angiogenesis, the main cause of ESCC mortality. Indeed, cytokines such as IFNγ , IL-27, IL-23, IL-12, and IL-2 have tumor suppressor functions, whereas pro-inflammatory cytokines, including IL-1, IL-6, and tumor necrosis factorα , have tumor-promoting [ 10 ]. Furthermore, experimental observation demonstrated that the overexpression of IL-1RA in IL-1 α expressing Kyse-410 EC cells decreased tumor cell proliferation and reduced expression of VEGF-A, a potent angiogenesis inducer. Additionally, chemokines and their receptors have been also shown to be important for EC prognosis. Indeed, CXCL12 and its receptor CXCR4 have been identified as biomarkers for ESCC and adenocarcinomas, while CXCL10, CCL4, and CCL5 have been shown to play an antitumoral role and prevent for ESCC progression [ 10 ]. Conversely, Tregs and Th17 cells recruited by tumor cell-derived chemokines CCL17 and CCL22 act by promoting EC pathogenesis [9]. The limited reliability of many traditional approaches combined with lack of specific symptoms in early stage ESCC results in most patients being diagnosed at advanced stages [ 4 , 11 ]. For this reason, the associations of therapies have been indicated as more beneficial than surgery alone [ 11 ]. Therefore, new biomarkers are essential for early diagnosis of ESCC. Indeed, when ESCC is diagnosed at an early stage, the 5-year overall survival rate can reach up to 80–90% [ 11 – 13 ]. Several potential biomarkers have been investigated, including p53 antibody, squamous cell cancer antigen (SCC-Ag) and carcinoembryonic antigen (CEA) [ 14 , 15 ]. While current evidence suggests that these and other markers may have diagnostic value, their sensitivity and specificity are not satisfactory [ 16 ]. In a recent study, the diagnostic potential of C-X-C motif ligand 8 (CXCL-8) levels in the sera of patients was compared with classical tumor markers (CEA and SCC-Ag) and the well-established marker of inflammation—C-reactive protein [ 14 ]. This study showed that the statistical parameters of CXCL-8 were higher than the classical tumor markers [ 14 ]. These data suggest that cytokines may be useful biomarkers to develop a low-cost, non-invasive and Cancers 2023,15, 1997 3 of 16 convenient method for routine diagnosis of ESCC and patient follow-up. However, given the complexity of ESCC and the pleotropic nature of cytokines, more studies are necessary to determine the potential of cytokines in the early ESCC diagnosis patient follow-up. The aim of this work was to determine the association between ESCC progression and the systemic levels of inflammatory cytokines to define their usefulness as diagnostic biomarkers. To help in this regard, we herein determined the association between a panel of circulating cytokines and the progression of ESCC. 2. Materials and Methods 2.1. Human Samples 2.1.1. Patients and Healthy Volunteers Blood samples were collected (convenience sampling) from 70 ESCC patients referred for upper digestive endoscopy at Barretos Cancer Hospital. The control group included 70 healthy volunteers, matched for age and gender, recruited from healthy blood donors at the same hospital. Figure S1 present the flowchart of inclusion of patients in the study. The complete information of both patients and healthy volunteers was previously described and published [ 17 , 18 ]. Diagnoses were performed by histological examination of samples obtained during endoscopy, before any treatment or primary esophageal surgery. The ethic committees of the participating institutions approved this study. Written informed consent was obtained from all subjects. 2.1.2. Blood Sample Collection and Analysis of Serum Cytokines Peripheral venous blood (4 mL) was collected using Ethylenediamine tetraacetic acid (EDTA) tubes (BD Vacutainer, BD Biosciences, Franklin Lakes, NJ, USA) and centrifuged at 2125 × gfor 10 min at 4 ◦ C. The supernatant (plasma) was separated, aliquoted and frozen at − 80 ◦ C until used. Blood samples from patients were collected prior to endoscopy and before treatment. Samples were identified with the hospital record (HR) number of each patient and healthy donor. Plasma level of IL-1 β , IL-6, IL-8, IL-10, tumor necrosis factorα (TNFα ) and IL-12p70 were measured using the cytometric bead assay (BD Biosciences, San Jose, CA, USA) (#551811) following the manufacturer’s instructions, and as previously described [ 19 , 20 ]. Samples were acquired in a BD FACSCanto ™ platform (BD Biosciences, San Jose, CA, USA) and analyzed with FACSDiva and FCAP Array ™ software (BD Biosciences). The quantification of cytokines by flow cytometry was performed once and in unicate, following the manufacturer’s instructions. The samples processed as one batch. 2.2. Animal Model 2.2.1. Cell Lines and Animals The human esophageal squamous cancer cell lines, Kyse-30 (#94072011) and Kyse-410 (#94072023), were purchased from Sigma (St. Louis, MO, USA). Kyse-30 derived from well-differentiated invasive ESCC resected from the middle intra-thoracic esophagus of an untreated 64-year-old male. It is characterized by p53 mutation and amplification of cERB B, MYC and CYCLIN D1, and has a doubling time of 20.8 h [ 21 ]. On the other hand, Kyse-410 was established from a poorly differentiated invasive ESCC resected from the cervical esophagus of an untreated 51-year-old male. It is characterized by heparin binding growth factor (hst-1) and cyclin D overexpression, and a doubling time of approximately 45 hours [22]. Both cell lines were cultured at 37 ◦ C in 5% CO 2 and 95% humidity atmosphere in RPMI (#11875093) supplemented with 10% heat-inactivated fetal bovine serum (#10270106), 1% L-glutamine (#25030-024), 1% HEPES (#15630-056), 1% sodium pyruvate (#11360056) and 1% pennicilin/streptomycin (#15070-063) (all from Gibco (Waltham, MA, USA), Invitrogen (Waltham, MA, USA)), until reaching 80% confluence. At this stage, cells were tested and confirmed negative for mycoplasma, and frozen in 1 ml aliquots in liquid nitrogen. Seven days before an animal experiment, one aliquot of each cell line was thawed Cancers 2023,15, 1997 4 of 16 and grown for 1 day in a T25 flask. Cells were then cultured in T75 flasks and passed every 48 h until injected in mice. NOD.Cg-Prkdc scid Il2rg tm1Wjl /SzJ (NSG) mice were originally obtained from the Jackson Laboratory (stock number: 005557, https://www.jax.org/strain/005557 (accessed on 9 October 2020)) (Bar Harbor, ME, USA) and maintained in our animal facility. All mice used in this study were ageand sex-matched and between the ages of 8 and 12 weeks. Male NSG mice were injected subcutaneously with 1 × 10 6 Kyse-30 or Kyse-410 cells in the left hind flank. Tumor growth was measured using a caliper and the volume was calculated using the following formula: ( π xd 2× D)/6 × 1000, d = small diameter and D = large diameter. Mice were sacrificed by CO 2 inhalation and the tumors dissected and processed for RNA extraction, flow cytometry and histological analysis. All animal experiments were performed according to recommendations of the European Union Directive 2010/63/EU and were previously approved by the Subcomissão de Ética para as Ciências da Vida e da Saúde (SECVS 074/2016) and the Portuguese National Authority Direcção Geral de Alimentação e Veterinária (014072). 2.2.2. Flow Cytometry Analysis Aseptically excised tumors were sectioned and incubated at 37 ◦ C for 30 min with collagenase D (#11088866001, 0.7 mg/mL, Sigma) and disrupted into single-cell suspensions by passage through a 70µ m-nylon cell strainer (#352350, BD Biosciences). Tumor single-cell suspension was then treated with erythrocyte lysis buffer (0.87% of NH 4 Cl). In order to remove cell debris, tumor single-cell suspension was further subjected to a 40:80% Percoll gradient (#17089101, GE Healthcare, Chicago, IL, USA). The resulting cell suspension was washed twice and counted. For flow cytometry analysis, single-cell suspension was stained with fluorochrome-conjugated antibodies for 30 min on ice. Antibodies specific for CD11b (#101216, clone M1/70; dilution 1:100) and Ly6G (#127645, clone 1A8; dilution 1:100) were obtained from BioLegend (San Diego, CA, USA). Data were acquired on a LSRII flow cytometer (BD Biosciences) with Diva Software and analyzed using FlowJo software (BD Biosciences). The total number of cells was determined based on the percentage of cells determined by flow cytometry and the total number of cells counted. 2.2.3. Real-Time RT-PCR Total RNA was extracted using Triple XTractor (GB023.0100, Grisp, Porto, Portugal) according to the manufacturer’s instructions. cDNA was generated from 1 µ g of RNA using the GRS cDNA Synthesis Master Mix (GK81.0100, Grisp) following the manufacturer’s instructions. The resultant cDNA template was used to quantify the expression of target genes by real-time PCR (Bio-Rad CFX96 Real-Time System with C1000 Thermal Cycler), and normalized to Ubiquitin mRNA levels using the ∆ Ct method (1.8ˆ(Housekeeping gene mRNA expression—Target gene mRNA expression) × 100,000). Target gene mRNA expression was quantified using SYBR Green (Thermo Fisher Scientific, Waltham, MA, USA) and specific oligonucleotides (Invitrogen). 3. Statistical Analyses The association of sociodemographic and habits between groups was performed by Chi-square test. The comparison of cytokine levels between the groups was verified by Mann– Whitney test, and the normality of data was analyzed by Kolmogorov–Smirnov test. The Odds Ratio (OR) was estimated by logistic regression in the context of multivariate analysis. Cytokine dosages were initially treated as quantitative variables to compare the case and control groups. The ROC curve was used to determine the cut off for cytokine values in order to discriminate patients who died among the case group. In survival analyzes, cytokines were treated as qualitative variables. Kaplan–Meier and the comparison between the curves by log-rank test estimated the survival curve. To select the characteristics for multivariate analysis, we adopted a significance level of 20%. To adjust the model, only individuals who had information on all variables (n= 56) were considered, so no specific Cancers 2023,15, 1997 5 of 16 treatment for missing data was performed. For the selection of variables, we applied the Backward Stepwise method, considering a significance level of 5% to adjust the final model. Despite the sample size of 56 participants, the model presented a power greater than 0.90 when we considered IL12, IL8 or TNF as variables of interest (Supplementary Table S1). To verify the assumption of proportional hazards in the Cox regression model, we used a descriptive method that consists of estimating ˆ Λ0(t)according to the expression below: ˆ Λ0(t)=∑ j:tj<t dj ∑ Cancers2023,15,xFORPEERREVIEW5of16   3.StatisticalAnalyses Theassociationofsociodemographicandhabitsbetweengroupswasperformedby Chi‐squaretest.Thecomparisonofcytokinelevelsbetweenthegroupswasverifiedby Mann–Whitneytest,andthenormalityofdatawasanalyzedbyKolmogorov–Smirnov test.TheOddsRatio(OR)wasestimatedbylogisticregressioninthecontextof multivariateanalysis. Cytokinedosageswereinitiallytreatedasquantitativevariablestocomparethecase andcontrolgroups.TheROCcurvewasusedtodeterminethecutoffforcytokinevalues inordertodiscriminatepatientswhodiedamongthecasegroup.Insurvivalanalyzes, cytokinesweretreatedasqualitativevariables.Kaplan–Meierandthecomparison betweenthecurvesbylog‐ranktestestimatedthesurvivalcurve.Toselectthe characteristicsformultivariateanalysis,weadoptedasignificancelevelof20%.Toadjust themodel,onlyindividualswhohadinformationonallvariables(n=56)wereconsidered, sonospecifictreatmentformissingdatawasperformed.Fortheselectionofvariables,we appliedtheBackwardStepwisemethod,consideringasignificancelevelof5%toadjust thefinalmodel.Despitethesamplesizeof56participants,themodelpresentedapower greaterthan0.90whenweconsideredIL12,IL8orTNFasvariablesofinterest (SupplementaryTableS1). ToverifytheassumptionofproportionalhazardsintheCoxregressionmodel,we usedadescriptivemethodthatconsistsofestimating𝛬 󰆹󰇛𝑡󰇜accordingtotheexpression below:    𝛬 󰆹 󰇛𝑡󰇜 𝑑𝑗 ∑ exp 󰇝𝑋󰆒𝛽 󰆹 󰇞  ∈ :  where𝑑isthenumberofeventsineachofthestratain𝑡.Iftheassumptionof proportionalityisvalid,thelog((𝛬 󰆹󰇛𝑡󰇜󰇜versustgraphisexpectedtoshowapproximately constantdifferencesovertime.Inaddition,wecalculatedPearson’scorrelationcoefficient betweentimesandstandardizedSchoenfeldresidualsforeachofthecovariates. Coefficientsclosetozeroareexpectedtoshownoevidencetorejecttheassumptionof proportionalrisks.Inthisway,webelievethattheassumptionofproportionalrisksis validsincethelog𝛬 󰆹󰇛𝑡󰇜󰇜 versustgraphs,foreachcovariate,presentconstantdistance overtimeandSupplementaryTableS2showsthatPearson’scorrelationcoefficientsdo notdisplayasignificantdifference(p>0.05).Theseanalyzescarriedoutwithsamples fromhumanswereperformedwithIBMSPSSStatisticsoftware21.0(SPSS,Chicago,IL, USA).pvalues≤0.05wereconsideredstatisticallysignificant. Fortheanalysisofsamplesfromtheanimalmodel,thedatawererepresentedas mean±standarderrorofthemean(SEM).TwomodesofANOVAwithmultiplepost‐test BonferronicomparisonsandStudent’st‐testwereusedforstatisticalcomparisons.p values≤0.05wereconsideredstatisticallysignificant. 4.Results 4.1.CharacterizationoftheStudyPopulationandRiskFactorstoDevelopDisease Whencomparedwiththecontrolgroup,thecasegrouphadahigherfrequencyof individualslivinginruralareas(p=0.007)thatconsumemorealcoholinthepast(p< 0.001)andtobacconowadays(p<0.001).Multivariateanalysisofthesignificantvariables wasperformed,andasignificancelevelofα=0.20wasadopted.Allthestatically significantvariablesobtainedintheunivariateanalysis,withap<0.20,wereincluded. Themultivariateanalysisperformedincludeddegreeofeducation,placeofresidence, alcoholconsumption,smoking,exposuretopesticideorinsecticide,physicalactivity (SupplementaryTableS3).Themajorityofthecasegroupwasalsomoreexposedto pesticidesandinsecticides(p<0.001),anddidnotperformphysicalactivity(p=0.024) (SupplementaryTableS3). ∈RjexpX0 lˆ β where dj is the number of events in each of the strata in tj . If the assumption of proportionality is valid, the log (( ˆ Λ0(t)) versus t graph is expected to show approximately constant differences over time. In addition, we calculated Pearson’s correlation coefficient between times and standardized Schoenfeld residuals for each of the covariates. Coefficients close to zero are expected to show no evidence to reject the assumption of proportional risks. In this way, we believe that the assumption of proportional risks is valid since the log ˆ Λ0(t)) versus t graphs, for each covariate, present constant distance over time and Supplementary Table S2 shows that Pearson’s correlation coefficients do not display a significant difference (p> 0.05). These analyzes carried out with samples from humans were performed with IBM SPSS Statistic software 21.0 (SPSS, Chicago, IL, USA). pvalues ≤ 0.05 were considered statistically significant. For the analysis of samples from the animal model, the data were represented as mean ± standard error of the mean (SEM). Two modes of ANOVA with multiple post-test Bonferroni comparisons and Student’s t-test were used for statistical comparisons. pvalues ≤0.05 were considered statistically significant. 4. Results 4.1. Characterization of the Study Population and Risk Factors to Develop Disease When compared with the control group, the case group had a higher frequency of individuals living in rural areas (p= 0.007) that consume more alcohol in the past (p< 0.001) and tobacco nowadays (p< 0.001). Multivariate analysis of the significant variables was performed, and a significance level of α = 0.20 was adopted. All the statically significant variables obtained in the univariate analysis, with a p< 0.20, were included. The multivariate analysis performed included degree of education, place of residence, alcohol consumption, smoking, exposure to pesticide or insecticide, physical activity (Supplementary Table S3). The majority of the case group was also more exposed to pesticides and insecticides (p< 0.001), and did not perform physical activity (p= 0.024) (Supplementary Table S3). This analysis also estimated the OR of those with a risk factor to develop the disease and the odds of those without a risk factor to develop the disease. We determined that only alcohol consumption, tobacco, exposure to pesticides or insecticides remained as statistically significant variables (p< 0.05, Table 1. Specific to alcohol consumption, individuals who drank in the past (and stopped more than 12 months ago) have a 14.5-fold increased chance of developing the disease in relation to those who never drank (95% Confidence interval (CI): 1.3–163.6, p= 0.030). Regarding the use of tobacco, individuals who currently smoke have a 11.3-fold increased chance of developing esophageal cancer compared to those who have never smoked (95% CI: 2.9–44.3, p< 0.001). Individuals exposed to pesticides and insecticides displayed results of 3.2-fold increased chance of developing the disease compared to those who were not exposed (95% CI: 1.1–9.4, p= 0.033). Taken together, these data corroborate alcohol consumption and tobacco usage as risk factors for ESCC, according to International Agency for Research on Cancer [23]. Cancers 2023,15, 1997 6 of 16 Table 1. Multiple logistic regression model of the statistically significant variables between the groups (case/control). Variable/Category N Cases (N Events) OR (CI95%) p Alcohol Never 15 (3) 1 - Yes, currently (if it was stopped in the last 12 months) 85 (36) 3.9 (0.4–35.9) 0.227 Yes, in the past 40 (31) 14.5 (1.3–163.6) 0.030 Tobacco Never 47 (10) 1 - Yes, currently (if it was stopped in the last 12 months) 58 (47) 11.3 (2.9–44.3) <0.001 Yes, in the past 35 (13) 1.5 (0.3–6.6) 0.581 Exposure to pesticide or insecticide No 78 (28) 1 - Yes 60 (42) 3.2 (1.1–9.4) 0.033 IL-8 140 (70) 1.6 (1.3–2.0) <0.001 N: number; OR: Odds Ratio; CI 95%: Confidence interval at the level of 95%; IL: Interleukin. Utilized test: Wald test. Statistically significant if p< 0.05. Regarding the clinicopathological features, 57.4% of the tumors were localized in the middle third of the esophagus, 53.8% were moderately differentiated, and 71.2% were classified with a TNM staging of III and IV [24] (Supplementary Table S4). 4.2. Serum Cytokine Analysis Reveals a Subset of Cytokines Differentially Produced by ESCC Patients From the initial set of 6 cytokines, we determined higher levels of IL-1 β (p= 0.032), IL-6 (p< 0.001), IL-8 (p< 0.001), and IL-10 (p= 0.027) in ECSS patients than in controls (Table 2). Multivariate analysis, including the cytokines IL-1 β , IL-6, IL-8, and IL-10 were significantly altered between cases and controls (Table 2), revealed that only IL-8 remained statistically significant (OR: 1.6–95% CI: 1.3–2.0, p< 0.001) (Table 1). The data that emerged from multivariate analysis suggest that high systemic level of IL-8, but not IL-6, is associated with ESCC progression, despite the fact that IL-6 was highly significant for poor overall survival estimate by Kaplan–Meier analysis (Table 3). Table 2. Comparison of the plasma levels of cytokines between study groups (case/control). Cytokines Groups Case Control p Mean (SD) pg/mL Median (Min–Max) pg/mL Mean (SD) pg/mL Median (Min–Max) pg/mL IL-12p70 0.78 (0.99) 0.39 (0.00–4.83) 0.93 (1.61) 0.28 (0.00–11.51) 0.991 TNF-α0.95 (2.24) 0.05 (0.00–16.37) 0.86 (2.14) 0.05 (0.00–14.27) 0.772 IL-10 12.98 (97.53) 0.68 (0.00–816.59) 0.55 (0.78) 0.42 (0.00–5.73) 0.027 IL-6 1157.20 (9614.02) 6.72 (0.21–80446.68) 2.07 (2.49) 1.62 (0.00–18.18) <0.001 IL-1β1.21 (3.07) 0.49 (0.00–24.60) 0.66 (1.87) 0.00 (0.00–13.76) 0.032 IL-8 196.31 (1529.42) 7.51 (2.40–12806.73) 3.52 (3.05) 3.16 (0.64–25.59) <0.001 Total 70 70 SD: standard deviation; pg/mL: picograms per milliliter; IL: Interleukin; TNFα : Tumor necrosis factor α . Utilized test: Mann–Whitney test. Statistically significant if p<0.05. Cancers 2023,15, 1997 7 of 16 Table 3. Overall survival estimated by Kaplan–Meier method considering socio-demographic, lifestyle, clinicopathological data and plasma levels of cytokine variables. Variable/Category n Cases (n Events) % Probability of Survival p 12 Months 36 Months 60 Months Overall survival 70 (52) 60.8 36.0 21.5 - Sex Female 11 (7) 60.0 50.0 18.8 0.403 Male 59 (45) 60.7 33.2 21.1 Race White 50 (38) 64.0 35.1 23.6 0.208 Non-white 18 (14) 48.1 34.4 0.00 Exposure to pesticide or insecticide No 28 (21) 64.3 46.4 26.7 0.360 Yes 42 (31) 58.6 28.0 17.4 Place residence Urban area only 12 (8) 66.7 41.7 41.7 0.553 Rural area only 8 (7) 62.5 25.0 12.5 Both areas 50 (37) 59.1 36.5 17.7 Alcohol Never/In the past 34 (26) 58.0 36.7 17.8 0.839 Yes, currently (if it was stopped in the last 12 months) 36 (26) 63.5 35.1 24.6 Tobacco Never 10 (8) 30.0 20.0 20.0 0.315 Yes, currently (if it was stopped in the last 12 months) 47 (35) 60.8 39.1 20.3 Yes, in the past 13 (9) 84.6 38.5 28.8 Degree of differentiation Well-differentiated 9 (7) 55.6 22.2 22.2 0.807 Moderately differentiated 35 (26) 56.4 37.6 23.9 Little-differentiated 21 (16) 66.0 30.5 16.9 TNF-α <0.825 48 (39) 58.3 25.9 18.9 0.100 ≥0.825 22 (13) 66.7 61.2 28.5 IL-10 <1.34 50 (38) 59.1 29.6 22.0 0.470 ≥1.34 20 (14) 65.0 53.2 19.7 IL-6 <4.7 27 (14) 80.4 64.3 45.1 <0.001 ≥4.7 43 (38) 48.8 18.2 6.9 IL-1β <1.35 52 (40) 62.6 29.2 19.6 0.364 ≥1.35 18 (12) 55.6 55.6 28.8 IL-8 <6.75 29 (16) 89.1 50.4 41.2 0.001 ≥6.75 41 (36) 41.5 25.9 9.3 Surgery No 44 (40) 47.1 16.5 8.2 <0.001 Yes 20 (9) 90.0 80.0 51.3 Cancers 2023,15, 1997 8 of 16 Table 3. Cont. Variable/Category n Cases (n Events) % Probability of Survival p 12 Months 36 Months 60 Months Radiotherapy No 25 (22) 56.0 28.0 7.0 0.117 Yes 43 (29) 61.8 41.2 29.7 Chemotherapy No 24 (20) 41.7 33.3 15.6 0.186 Yes 45 (32) 70.3 36.3 24.6 TNM I/II 17 (8) 87.8 81.6 49.8 <0.001 III/IV 42 (38) 52.4 16.7 9.5 Topography of the tumor Upper/Middle third 33 (27) 69.7 42.4 20.5 0.447 SOE 19 (14) 51.3 22.8 22.8 n: Number; IL: Interleukin; TNFα : Tumor necrosis factor α ; TNM staging: System based on the size and/or extent of the primary tumor (T), amount of compromised lymph nodes (N) and presence of metastases (M). Utilized test: Log-rank. Statistically significant if p< 0.05. The relationship between the levels of the different cytokines and survival was established using the cutoff point obtained with the ROC curve (Supplementary Table S5); based on these data, individuals were categorized into two distinct groups: low and high cytokine producers (Supplementary Table S6). In addition to cytokines, in this analysis, we also included the socio-demographic characteristics, lifestyle and clinical-pathological data of our cohort. The time of overall survival was calculated as the interval between the date of diagnosis and the date of death from cancer or the date of the last information. Kaplan–Meier curves are represented in Supplementary Figures S2 and S3. Using the Kaplan–Meier analyses and log-rank test, we observed that only IL-6, IL-8, surgery and TNM [ 24 ] staging had significant influence on the overall survival rate at 12, 36 and 60 months (Table 3). Individuals who underwent surgery had significantly better overall survival rate (90.0% at 12 months, 80.0% at 36 months and 51.3% at 60 months) when compared to individuals who did not undergo surgery (47.1% at 12 months, 16.5% at 36 months and 8.2% at 60 months). Additionally, individuals with TNM [ 24 ] I/II staging at the time of diagnosis displayed significantly better overall survival rates (87.8% at 12 months, 81.6% at 36 months and 49% at 60 months) than individuals with advanced TNM [ 24 ] staging of III/IV (52.4% at 12 months, 16.7% at 6 months and 9.5% at 60 months). Regarding the systemic level cytokines, we determined that individuals with low levels of IL-6 had significantly higher overall survival rate (80.4% at 12 months, 64.3% at 36 months and 45.1% at 60 months) when compared to individuals with high levels of IL-6 (48.8% at 12 months, 18.2% at 36 months and 6.9% at 60 months). Similar data were obtained for IL-8, wherein patients with low levels of this cytokines had notably higher overall survival rates (89.1% in 12 months, 50.4% in 36 months and 41.2% in 60 months) than patients with high levels of IL-8 (41.5% at 12 months, 25.9% at 36 months and 9.3% at 60 months). Overall survival rates correlating IL-6, IL-8, TNM and Surgery are shown in Figure 1. These data show that the pro-inflammatory cytokines IL-6 and IL-8 are closely related to tumor growth and progression. In this regard, these cytokines have been shown to promote immune escape, epithelial–mesenchymal transition, and recruitment of myeloid-derived suppressor cells thus being associated with poor prognosis in many malignant tumors [ 25 , 26 ]. To identify the clinical value of the tested cytokines in cancer diagnosis, the area under the curve (AUC) was calculated (Supplementary Table S5). For each cytokine, we established an AUC > 0.70 (or 70%). AUC of IL-8 (76%) was the highest of all the cytokines together with IL-6. The specificity was also the highest of all cytokines (71%), whereas the sensitivity was the lowest (73.3%). IL-8 showed high sensitivity correctly discriminating individuals with cancer. Cancers 2023,15, 1997 9 of 16 Figure 1. Graphs of relative expression of genes of interest performed by real-time PCR. Graphs representing the relative expression of the genes ( a ) CXCL-2; ( b ) CXCL-1; ( c ) CCL2; and ( d ) VEGF in the tumors induced by the Kyse-30 and Kyse-410 cell lines. “BDL: Below Detection Levels”. Data represent 10 animals (5 per group) from the same experiment. Each dot represents one animal; the blue dots indicate the relative expression of the different genes of each animal inoculated with Kyse-30 and the red dots of each animal inoculated with Kyse-410. All experiments were performed only once. Statistical significance was calculated by using unpaired t-test and pvalues are shown in the figure. Multivariate analysis using COX regression was performed to calculate the Hazard Ratio estimates (HR) (95% confidence intervals (CI)) for the significant variables (p< 0.005) (Table 4). Individuals who have high levels of IL-12p70 have a lower risk of death when compared to those with lower levels of this cytokine (HR: 0.34–95% CI: 0.14–0.79). Similarly, individuals with high level of TNFα have a lower risk of death when compared to those with low TNFα levels (HR: 0.23–95% CI: 0.09–0.59). In contrast to IL-12p70 and TNFα , IL-8 appears to be a risk factor for ESCC, since individuals with higher levels of IL-8 had increased risk of dying than individuals with low levels of IL-8 (HR: 4.56–95% CI: 2.21–9.41). As expected, patients who underwent surgery and radiotherapy had lower risk of dying when compared to those who did not undergo surgery or did not undergo radiotherapy, (HR: 0.21–95% CI: 0.08–0.55) and (HR: 0.27–95% CI: 0.14–0.54), respectively. Patients with early-stage TNM [ 24 ] (I/II) also had a lower risk of dying when compared to those with advanced TNM [24] (III/IV) (HR: 3.68–95% CI: 1.45–9.37) (Table 4). Cancers 2023,15, 1997 16 of 16 40. 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