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Additional file 3.

Additional file 2.

As major components of the tumor microenvironment (TME), tumor-associated macrophages (TAMs) play an exceedingly complicated role in tumor progression and tumorigenesis. However, few studies have reported the specific TAM gene signature in bladder cancer. Herein, this study focused on developing a TAM-related prognostic model in bladder cancer patients based on The Cancer Genome Atlas (TCGA) data. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify key genes related to TAM (M2 macrophage). Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis showed the functional categories of the key genes. Simultaneously, we used the Least Absolute Shrinkage and Selection Operator (LASSO) and univariate and multivariate Cox regressions to establish a TMA-related prognostic model containing six key genes: TBXAS1, GYPC, HPGDS, GAB3, ADORA3, and FOLR2. Subsequently, single-cell sequencing data downloaded from Gene Expression Omnibus (GEO) suggested that the six genes in the prognostic model were expressed in TAM specifically and may be involved in TAM polarization. In summary, our research uncovered six-TAM related genes that may have an effect on risk stratification in bladder cancer patients and could be regarded as potential TAM-related biomarkers.

Hepatic alveolar echinococcosis (HAE) and liver cancer had similarities in imaging results, clinical characteristics, and so on. And it is difficult for clinicians to distinguish them before operation. The aim of our study was to build a differential diagnosis nomogram based on platelet (PLT) score model and use internal validation to check the model. The predicting model was constructed by the retrospective database that included in 153 patients with HAE (66 cases) or liver cancer (87 cases), and all cases was confirmed by clinicopathology and collected from November 2011 to December 2018. Lasso regression analysis model was used to construct data dimensionality reduction, elements selection, and building prediction model based on the 9 PLT-based scores. A multi-factor regression analysis was performed to construct a simplified prediction model, and we added the selected PLT-based scores and relevant clinicopathologic features into the nomogram. Identification capability, calibration, and clinical serviceability of the simplified model were evaluated by the Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic curve (ROC), and decision curve. An internal validation was also evaluated by the bootstrap resampling. The simplified model, including in 4 selected factors, was significantly associated with differential diagnosis of HAE and liver cancer. Predictors of the simplified diagnosis nomogram consisted of the API index, the FIB-4 index, fibro-quotent (FibroQ), and fibrosis index constructed by King’s College Hospital (King’s score). The model presented a perfect identification capability, with a high C-index of 0.929 (0.919 through internal validation), and good calibration. The area under the curve (AUC) values of this simplified prediction nomogram was 0.929, and the result of ROC indicated that this nomogram had a good predictive value. Decision curve analysis showed that our differential diagnosis nomogram had clinically identification capability. In conclusion, the differential diagnosis nomogram could be feasibly performed to verify the preoperative individualized diagnosis of HAE and liver cancer.