Purpose: We aimed to develop a combined predicting model for benign esophageal stenosis (BES) after simultaneous integrated boost (SIB) with concurrent chemotherapy in patients with esophageal squamous cell carcinoma (ESCC).Methods: This study included 65 patients with EC who underwent SIB with chemotherapy. Esophageal stenosis was evaluated using esophagograms and the severity of eating disorders. Risk factors were investigated using univariate and multivariate analyses. Radiomics features were extracted based on contrast-enhanced CT (CE-CT) before treatment. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for feature selection and radiomics signature construction. The model’s performance was evaluated using Harrell’s concordance index and receiver operating characteristic curves.Results: The patients were stratified into low- and high-risk groups according to BES after SIB. The area under the curves of the clinical model, Rad-score, and the combined model were 0.751, 0.820 and 0.864, respectively. In the validation cohort, the AUCs of these three models were 0.854, 0.883 and 0.917, respectively. The Hosmer-Lemeshow test showed that there was no deviation from model fitting for the training cohort (p=0.451) and validation cohort (p=0.481). The C-indexes of the nomogram were 0.864 and 0.958 for the training and validation cohort, respectively. The model combined with Rad-score and clinical factors achieved favorable prediction ability.Conclusion: Definitive chemoradiotherapy could alleviate tumor-inducing esophageal stenosis but result in benign stenosis. We constructed and tested a combined predicting model for benign esophageal stenosis after SIB. The nomogram incorporating both radiomics signature and clinical prognostic factors showed favorable predictive accuracy for BES in ESCC patients who received SIB with chemotherapy.Trial registration number and date of registration: Registered in www.Clinicaltrial.gov, ID: NCT01670409, August 12, 2012

Our previous study showed that levels of circulating insulin-like growth factor binding protein-1 (IGFBP-1) has potential diagnostic value for early-stage upper gastrointestinal cancers. This study aimed to assess whether serum IGFBP-1 is a potential diagnostic and prognostic biomarker for CRC patients. IGFBP-1 mRNA expression profile data of peripheral blood in colorectal cancer (CRC) patients were downloaded and analyzed from Gene Expression Omnibus database. We detected serum IGFBP-1 in 138 CRC patients and 190 normal controls using enzyme-linked immunosorbent assay. Blood IGFBP-1 mRNA levels were higher in CRC patients than those in normal controls (P = 0.027). In addition, serum IGFBP-1 protein levels in the CRC group were significantly higher than those in normal control group (P < 0.0001). Serum IGFBP-1 demonstrated better diagnostic accuracy for all CRC and early-stage CRC, respectively, when compared with carcinoembryonic antigen (CEA), carbohydrate antigen19-9 (CA 19-9) or the combination of CEA and CA19-9. Furthermore, Cox multivariate analysis revealed that serum IGFBP-1 was an independent prognostic factor for OS (HR = 2.043, P = 0.045). Our study demonstrated that serum IGFBP-1 might be a potential biomarker for the diagnosis and prognosis of CRC. In addition, the nomogram might be helpful to predict the prognosis of CRC.

The results regarding the association between insulin-like growth factor binding protein 1 (IGFBP1) expression and cancer risk were controversial. We performed a meta-analysis to provide novel evidence on relationship between IGFBP1 expression and cancer risk. PubMed, Embase, Cochrane library and Web of science were searched for relevant cohort and case-control studies exploring the relationship between IGFBP1 expression and cancer risk. Odds ratios (ORs) were pooled in this meta-analysis using random model. Subgroup analyses were performed based on ethnicity, tumor types, publication year, study type, Newcastle-Ottawa Scale (NOS) score and sex. A total of 27 studies including 16 cohort and 11 case-control studies were identified by literature search. No significant association was found between IGFBP1 expression and risk of various cancers [0.90, 95% confidence interval (CI): 0.79, 1.03]. The overall results showed that the pooled ORs were 0.71 (95% CI: 0.57, 0.88] for prostate cancer risk and 0.66 (95%CI: 0.44, 0.99) for colorectal cancer (CRC) risk. However, there is no significant association between IGFBP1 expression and risk for ovarian cancer (1.70, 95%CI: 0.41, 6.99), breast cancer (1.02, 95%CI: 0.85, 1.23), endometrial cancer (1.19, 95%CI: 0.64, 2.21), colorectal adenoma (0.93; 95%CI: 0.81, 1.07), lung cancer (0.81, 95%CI: 0.39, 1.68) or multiple myeloma (1.20, 95%CI: 0.98, 1.47). In this study, compared with individuals at low IGFBP1 expression adjusted for age, smoking status, alcohol intake and so on, risk of the prostate cancer and CRC were decreased among individuals of high IGFBP1 expression. There needs further study to confirm this issue.

Objective: The occurrence and development of oesophageal neoplasia (ON) is closely related to hormone changes. The aim of this study was to investigate the causal relationships between age at menarche (AAMA) or age at menopause (AAMO) and benign oesophageal neoplasia (BON) or malignant oesophageal neoplasia (MON) from a genetic perspective.Methods: Genome-wide association study (GWAS) summary data of exposures (AAMA and AAMO) and outcomes (BON and MON) were obtained from the IEU OpenGWAS database. We performed a two-sample Mendelian randomization (MR) study between them. The inverse variance weighted (IVW) was used as the main analysis method, while the MR Egger, weighted median, simple mode, and weighted mode were supplementary methods. The maximum likelihood, penalized weighted median, and IVW (fixed effects) were validation methods. We used Cochran’s Q statistic and Rucker’s Q statistic to detect heterogeneity. The intercept test of the MR Egger and global test of MR pleiotropy residual sum and outlier (MR-PRESSO) were used to detect horizontal pleiotropy, and the distortion test of the MR-PRESSO analysis was used to detect outliers. The leave-one-out analysis was used to detect whether the MR analysis was affected by single nucleotide polymorphisms (SNPs). In addition, the MR robust adjusted profile score (MR-RAPS) method was used to assess the robustness of MR analysis.Results: The random-effects IVW results showed that AAMA had a negative genetic causal relationship with BON (odds ratio [OR] = 0.285 [95% confidence interval [CI]: 0.130-0.623], P = 0.002). The weighted median, maximum likelihood, penalized weighted median, and IVW (fixed effects) were consistent with random-effects IVW (P < 0.05). The MR Egger, simple mode and weighted mode results showed that AAMA had no genetic causal relationship with BON (P > 0.05). However, there were no causal genetic relationships between AAMA and MON (OR = 1.132 [95%CI: 0.621-2.063], P = 0.685), AAMO and BON (OR = 0.989 [95%CI: 0.755-1.296], P = 0.935), or AAMO and MON (OR = 1.129 [95%CI: 0.938-1.359], P = 0.200). The MR Egger, weighted median, simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW (fixed effects) were consistent with a random-effects IVW (P > 0.05). MR analysis results showed no heterogeneity, the horizontal pleiotropy and outliers (P > 0.05). They were not driven by a single SNP, and were normally distributed (P > 0.05).Conclusion: Only AAMA has a negative genetic causal relationship with BON, and no genetic causal relationships exist between AAMA and MON, AAMO and BON, or AAMO and MON. However, it cannot be ruled out that they are related at other levels besides genetics.

Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. In present study, we aimed to identify a novel optimized autoantibody panel with high diagnostic accuracy for clinical and preclinical esophageal squamous cell carcinoma (ESCC) using machine learning (ML) algorithms. We identified potential autoantibodies against tumor-associated antigens with serological proteome analysis. Serum autoantibody levels were measured by ELISA. Using a training set (n = 531), 102 models based on ML algorithms were constructed, and Partial Least Squares Generalized Linear Models (plsRglm) was selected out using receiver operating characteristics (ROC), Kolmogorov–Smirnov (K-S) test, and Population Stability Index (PSI), and further validated through an internal validation set (n = 413), external validation set 1 (n = 371), and external validation set 2 (n = 202). Then, we validated the ability of plsRglm model in predicting preclinical ESCC by a nested case–control study (24 preclinical ESCCs and 112 matched controls) within a population-based prospective cohort study. ROC analysis, K-S test, and PSI showed that plsRglm model based on four autoantibodies (ALDOA, ENO1, p53, and NY-ESO-1) exhibited the better diagnostic performance and robustness, which provided a high diagnostic accuracy in diagnosing ESCC with the respective AUCs (sensitivities and specificities) of 0.860 (68.8% and 90.4%) in the training set, 0.826 (65.3% and 89.1%) in the internal validation set, and 0.851 (69.2% and 87.3%) in the external validation set 1. For early-stage ESCC, this signature also maintained diagnostic performance [0.817 (62.3% and 90.4%) in the training set; 0.842 (62.5% and 89.1%) in the internal validation set; 0.854 (63.2% and 87.3%) in the external validation set 1; and 0.850 (67.3% and 90.1%) in the external validation set 2]. In the nested case–control study, this plsRglm model could detect the presence of preclinical ESCC with the AUC of 0.723, sensitivity of 54.2%, and specificity of 86.6%. Our findings indicated that the plsRglm model based on four autoantibodies might help identify preclinical and early-stage ESCC.

Background: Previous studies have shown that ALDH2 and ADH1B genes may be associated with alcohol metabolism and the risk of esophageal squamous cell carcinoma (ESCC), with inconsistent results. This meta‐analysis aimed at comprehensively assessing the associations between ALDH2 and ADH1B polymorphisms and the risk of ESCC to synthesize and clarify the evidence.Methods: We calculated summary estimates of the associations between four genetic variants (rs671 and rs674 in ALDH2, and rs1229984 and rs1042026 in ADH1B) and the ESCC risk across 23 publications in the additive model and allelic model. Venice criteria, Bayesian false discovery probability (BFDP), and false‐positive reporting probability (FPRP) were used to assess the strength of epidemiological evidence. Heterogeneity among studies was evaluated by using the Higgin's I² statistic, and publication bias was assessed by using funnel plots and Begg's test. A Mendelian randomization (MR) analysis was performed to determine the causal association between alcohol intake and esophageal cancer risk. Data from the HaploReg v4.1 and PolyPhen‐2 were analyzed for functional annotations.Results: Of the four genetic variants, rs671 of ALDH2 was associated with a significantly reduced risk of ESCC (OR: 0.60, 95% CI: 0.50–0.73), whereas rs1229984 of ADH1B was associated with a significantly increased risk (2.50, 95% CI: 1.70–3.69) in the additive model. In the allelic model, the variant rs1229984 of ADH1B also increased the risk of ESCC (OR: 1.50; 95% CI: 1.21–1.87). The result for the variant rs671 was considered as strong epidemiological evidence. Functional annotations identified that the four variants were related to the enhancer histone marks and motif changes. The other two variants were not associated with the ESCC risk (rs674 of ALDH2 OR: 1.22, 95% CI: 0.71–2.12; rs1042026 of ADH1B OR: 1.28, 95% CI: 0.52–3.14) in the additive model. The MR analysis did not find a causal effect of alcohol on the esophageal cancer risk.Conclusions: The results showed that ADH1B rs1229984 was significantly associated with an increased the risk of ESCC.