PDX-1 regulates cell proliferation and invasion in human pancreatic cancer cells. Down-regulation of PDX-1 expression inhibits pancreatic cancer cell growth in vitro and in vivo, implying its use as a potential therapeutic target for the treatment of pancreatic cancer.

Background: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study, we investigated the association between radiomics features and the tumor histological subtypes, and we aimed to establish a nomogram for the classification of small cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). Methods: This was a retrospective single center study. In total, 468 cases including 202 patients with SCLC and 266 patients with NSCLC were enrolled in our study, and were randomly divided into a training set (n = 327) and a validation set (n = 141) in a 7:3 ratio. The clinical data of the patients, including age, sex, smoking history, tumor maximum diameter, clinical stage, and serum tumor markers, were collected. All patients underwent enhanced computed tomography (CT) scans, and all lesions were pathologically confirmed. A radiomics signature was generated from the training set using the least absolute shrinkage and selection operator algorithm. Independent risk factors were identified by multivariate logistic regression analysis, and a radiomics nomogram based on the radiomics signature and clinical features was constructed. The capability of the nomogram was evaluated in the training set and validated in the validation set. Results: Fourteen of 396 radiomics parameters were screened as important factors for establishing the radiomics model. The radiomics signature performed well in differentiating SCLC and NSCLC, with an area under the curve (AUC) of 0.86 (95% CI: 0.82-0.90) in the training set and 0.82 (95% CI: 0.75-0.89) in the validation set. The radiomics nomogram had better predictive performance [AUC = 0.94 (95% CI: 0.90-0.98) in the validation set] than the clinical model [AUC = 0.86 (95% CI: 0.80-0.93)] and the radiomics signature [AUC = 0.82 (95% CI: 0.75-0.89)], and the accuracy was 86.2% (95% CI: 0.79-0.92) in the validation set. Conclusion: The enhanced CT radiomics signature performed well in the classification of SCLC and NSCLC. The nomogram based on the radiomics signature and clinical factors has better diagnostic performance for the classification of SCLC and NSCLC than the simple application of the radiomics signature.

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One hundred and ninety-six untreated de novo acute myeloid leukemia (AML) patients were treated with homoharringtonine + cytosine arabinoside (HA) based induction therapy composed of three chemotherapeutic drugs (HAD/M, D-daunorubicin-DNR, M-mitozantrone-MTZ) used in our hospital for the past 12 years. The patient population was relatively young (median age 37, oldest patient 67), and patients were excluded if they had prior MDS or prior chemotherapy or radiotherapy. Complete remission (CR) rate, disease free survival (DFS) and overall survival (OS) of the patients were calculated. One hundred and fifty-three patients who had karyotype results were divided into four groups according to Southwestern Oncology Group (SWOG) criteria. Differences of CR rate, DFS and OS of different groups were evaluated. The CR rate of all 196 cases was 153/196 (78.1%), and 95.3% of these were within 1 - 2 courses. Median DFS of the 153 CR patients was 23.8 (range from 1.0 to 153) months. DFS rates at 3 years and 5 years were 41.1% and 35.9%, respectively. The median OS of 196 patients was 19.3 (0.5 - 154) months. The probabilities of 3-year and 5-year OS were 31.5% and 29.2%, respectively. CR rate, DFS and OS of the different cytogenetic risk groups were also be analyzed. According to SWOG criteria, patients were classified into favorable, intermediate, adverse and unknown (a group where the meaning of chromosomes are unclear) groups. CR rate, median DFS and OS were 91.9%, 90.8 months and 94.4 months for the favorable group; 86.4%, 22.0 months and 22.8 months for the intermediate group; 59.4%, 9 months and 10.5 months for the adverse group; 76.0%, 22.0 months, 16.1 months for the unknown group, respectively. The differences among the four groups were statistically significant (P = 0.001, 0.0033, 0.0001). We conclude that triple-drugs induction regimens based on HA (HAD/M) are highly effective in adult AML in China. Cytogenetics is the important prognostic factor. SWOG karyotype subtyping criteria was appropriate for our patients, the prognosis of the unknown group was similar to that of the intermediate group.