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Dysregulated expression of S100A7 is found in several cancers and plays an important role in tumor progression; however, its carcinogenic role in esophageal squamous carcinoma (ESCC) is still poorly understood. Here, we identified that the levels of S100A7 were remarkably upregulated in 341 tumor tissues (P <.001) and 274 serum samples (P <.001) of ESCC patients compared with normal control. It was an independent prognostic factor (P =.026). Furthermore, a new diagnostic model for ESCC based on serum S100A7, SCC, and crfra21‐1 was established with area under curve (AUC) up to 0.863 (95% CI: 0.802‐0.925). Mechanically, we found upregulated S100A7 could promote cell migration and proliferation through intracellular binding to JAB1 and paracrine interaction with RAGE receptors and then activates the downstream signaling pathways. In addition, exocrine S100A7 could promote M2 macrophage infiltration and polarization by up‐regulating M2 macrophage associated proteins, and tumor angiogenesis by enhancing the activation of p‐ErK and p‐FAK pathways. Further animal experiments confirmed the role of S100A7 in promoting M2 macrophage infiltration and angiogenesis in ESCC. In conclusion, these findings highlighted the potential diagnostic and prognostic value of S100A7 in patients with ESCC. Meanwhile, our results reveal that S100A7 promotes tumor progression by activating oncogenic pathways and remodeling tumor microenvironment, which paving the way for the progress of S100A7 as a therapeutic target for cancer treatment. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the leading cause of cancer-related death worldwide, and the timely and serial assessment of low-dose computed tomography (LDCT) in high-risk populations remains a challenge. Furthermore, testing a single biomarker for the diagnosis of lung cancers is of relatively low effectiveness. Thus, a stronger diagnostic combination of blood biomarkers is needed to improve the diagnosis of non-small cell lung cancer (NSCLC).Methods: The blood levels of individual biomarkers [IDH1, DNA methylation of short stature homeobox 2 gene (SHOX2), and prostaglandin E receptor 4 gene (PTGER4)] were measured and statistically analyzed in samples from healthy controls and patients with lung cancer. In total, 221 candidates were enrolled and randomly assigned into two groups for the training and validation of a diagnostic panel. Additionally, a subgroup analysis was performed in the whole cohort.Results: A newly combined 3-marker diagnostic model for lung cancers was established and validated with area under the receiver operating characteristic (ROC) curve (AUC) values ranging from 0.835 to 0.905 in independent groups showing significantly stronger diagnostic value compared with a single tested biomarker. The sensitivity of the diagnostic model was as high as 86.1% and 80.0% in the training and validation sets, respectively. Although no apparent differences were found between the 3-marker and 2-marker models, the high clinical T-stage and histological type specificity of IDH1 and two other methylated DNA biomarkers were demonstrated in the subgroup analysis.Conclusions: The combination of single biomarkers with high stage-specificity and histological type specificity (SHOX2 and PTGER4 DNA methylation and IDH1) showed better diagnostic performance in the detection of lung cancers compared with single marker assessment. A greater clinical utility of the panel may be developed by adding demographic/epidemiologic characteristics. [ABSTRACT FROM AUTHOR]

Objectives: Although low-dose computed tomography has been confirmed to have meaningful diagnostic utility, lung cancer is still the leading cause of cancer-related deaths for both genders worldwide. Thus, a novel panel with a stronger diagnostic performance for lung cancer is needed. This study aimed to investigate the efficacy of a new panel in lung cancer diagnosis. Materials and Methods: The serum levels of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125) and seven autoantibodies were measured and statistically analyzed in samples from healthy controls and patients with lung cancer. The 316 candidates enrolled in this study were randomly assigned into two groups for the training and validation of a diagnostic panel. Results: An optimal panel with four biomarkers (CEA, CA125, Annexin A1-Ab, and Alpha enolase-Ab) was established, with an area under the receiver operator characteristic (ROC) curve (AUC) of 0.897, a sensitivity of 86.5%, a specificity of 82.3%, a positive predictive value (PPV) of 88.3%, a negative predictive value (NPV) of 79.7%, and a diagnostic accuracy of 84.8% for the training group. The panel was validated, with an AUC of 0.856 and a sensitivity of 87.5% for the validation group. Furthermore, the new panel performed significantly better in lung cancer screening than did CEA and CA125 in all of the cohorts (p<.05). Conclusion: The diagnostic performance of CEA and CA125 was significantly enhanced through their combination with two autoantibodies (Annexin A1-Ab, and Alpha enolase-Ab). Optimization of the measured autoantibodies is critical for generating a panel to detect lung cancer in patients. [ABSTRACT FROM AUTHOR]