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The task of this article is to harness the wisdom of a crowd without calibration. We assume each expert to form predictions by linearly combining various information cues inspired by the lens model, and use Gaussian process to account for sampling and judgmental errors in quantile judgments. Without knowing the experts' observed information cues, we develop a three-step estimation algorithm to factor quantile judgments into "variable profiles" (latent cues underlying each variable of interest) and "expert profiles" (each expert's weights over these cues). We can inquire about expert similarity using their weights of the latent cues, which preserve the same clustering results as the actual weights of the observed cues up to a full-rank linear transform. We can then depict the diversity and dependency among experts explicitly and retain a subcrowd by picking delegates from each subgroup of experts based on the estimated weights. Simulation and case studies demonstrate that a subcrowd selected this way can represent the entire expert panel well. [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]

Understanding interactions between tumor and the host immune system holds great promise to uncover biomarkers for targeted therapies and clinical outcomes. However, systematical analysis of immune signatures in esophageal squamous cell carcinoma (ESCC) remains largely unstudied. In this study, immune signatures containing 708 immune related genes were curated from mRNA microarray data with tumor and paired normal tissues from 119 ESCC patients. Differential expression and survival analysis were performed with validations from Human Protein Atlas and an independent cohort of 110 ESCC patients by immunohistochemistry staining. We identified a total of 186 significantly dysregulated genes in ESCC, including downregulated genes SPINK5, IL1RN and upregulated genes SPP1 and PLAU, which were further confirmed in Human Protein Atlas data. Moreover, nine immune related genes (ABL1, ATF2, ATG5, C6, CD38, HMGB1, ICOSLG, IL12RB2 and PLAU) were significantly associated with patients' overall survival, among which, prognostic model was built including three independent factors ABL1, CD38 and ICOSLG. Validation by immunohistochemistry staining suggested that combination with tumor infiltrated CD4+ and CD8+ T lymphocytes would yield higher performance in distinguishing cases as high or low risk of unfavorable prognosis. In summary, we profiled the immune status in ESCC and established predictive and prognostic factors for ESCC, which could reflect immune disorders within tumor microenvironments and independently distinguish patients with a high risk of reduced survival, providing novel predictive and therapeutic targets for ESCC patients in the future. [ABSTRACT FROM PUBLISHER]