OCIAD2 promotes pancreatic cancer progression through the AKT signaling pathway.

BACKGROUND: OCIAD2（Ovarian carcinoma immunoreactive antigen-like protein 2） is a protein reported in various cancers. However, the role of OCIAD2 has not been explored in pan-cancer datasets. The purpose of this research lies in analyzing the expression level and prognostic-related value of OCIAD2 in different human cancers, as well as revealing the underlying mechanism in specific cancer type (pancreatic adenocarcinoma, PAAD). METHODS: The correlation between OCIAD2 expression level and clinical relevance in different human cancers was investigated from bioinformatical perspective (GTEx and TCGA). The OCIAD2 expression level and clinical significance in PAAD were explored in GEO datasets and tissue microarray. Functional experiments were used to determine the OCIAD2 cell functions in vitro and in vivo. GSEA, western blot and immunohistochemistry were used to uncover the potential mechanism. RESULTS: OCIAD2 expression level was closely correlated with clinical relevance in many cancer types through pan-cancer analysis, and we found OCIAD2 was highly expressed in PAAD and associated with poorer prognosis. OCIAD2 acted as the promotor of Warburg effect and influenced PAAD cells proliferation, migration and apoptosis. Mechanistically, OCIAD2 upregulation may boost glycolysis in PAAD via activating the AKT signaling pathway in PAAD. CONCLUSIONS: In PAAD, OCIAD2 promotes Warburg effect via AKT signaling pathway and targeting cancer cells metabolic reprogramming could be a potential treatment.

Immunosuppression, immune escape, and immunotherapy in pancreatic cancer: focused on the tumor microenvironment.

Pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, is characterized by poor treatment response and low survival time. The current clinical treatment for advanced PDAC is still not effective. In recent years, the research and application of immunotherapy have developed rapidly and achieved substantial results in many malignant tumors. However, the translational application in PDAC is still far from satisfactory and needs to be developed urgently. To carry out the study of immunotherapy, it is necessary to fully decipher the immune characteristics of PDAC. This review summarizes the recent progress of the tumor microenvironment (TME) of PDAC and highlights its link with immunotherapy. We describe the molecular cues and corresponding intervention methods, collate several promising targets and progress worthy of further study, and put forward the importance of integrated immunotherapy to provide ideas for future research of TME and immunotherapy of PDAC.

Exploitation and Verification of a Stroma- and Metastasis-Associated Risk Prognostic Signature in Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD), one of the most malignant tumors, not only has abundant mesenchymal components, but is also characterized by an extremely high metastatic risk. The purpose of this study was to construct a model of stroma- and metastasis-associated prognostic signature, aiming to benefit the existing clinical staging system and predict the prognosis of patients. First, stroma-associated genes were screened from the TCGA database with the ESTIMATE algorithm. Subsequently, transcriptomic data from clinical tissues in the RenJi cohort were screened for metastasis-associated genes. Integrating the two sets of genes, we constructed a risk prognostic signature by Cox and LASSO regression analysis. We then obtained a risk score by a quantitative formula and divided all samples into high- and low-risk groups based on the scores. The results demonstrated that patients with high-risk scores have a worse prognosis than those with low-risk scores, both in the TCGA database and in the RenJi cohort. In addition, tumor mutation burden, chemotherapeutic drug sensitivity and immune infiltration analysis also exhibited significant differences between the two groups. In exploring the potential mechanisms of how stromal components affect tumor metastasis, we simulated different matrix stiffness in vitro to explore its effect on EMT key genes in PAAD cells. We found that cancer cells stimulated by high matrix stiffness may trigger EMT and promote PAAD metastasis.

[Hematoma Segmentation of Spontaneous Intracerebral Hemorrhage Based on Watershed and Region-Growing Algorithm].

Objective: To establish a brain hematoma CT image segmentation method based on watershed and region-growing algorithm so as to measure hematoma volume quickly and accurately, to explore the consistency between the results of this segmentation method and those of manual segmentation, the clinical gold standard, and to compare the results of this method with the calculation of the two Tada formulas commonly used in clinical practice. Methods: The preoperative CT images of 152 patients who were treated for spontaneous cerebral hemorrhage at the Department of Neurosurgery, West China Hospital, Sichuan University between January 2018 and June 2019 were retrospectively collected. The CT images were randomly assigned, by using a random number table, to the training set, the test set and the validation set, which contained 100 patients, 22 patients and 30 patients, respectively. The labeling results of the training set and the test set were used in algorithm training and testing. Four methods, namely, manual segmentation, algorithm segmentation, i.e., segmentation calculation based on watershed and regional growth algorithm, Tada formula, i.e., the traditional Tada formula calculation, and accurate Tada formula, i.e., accurate Tada formula calculation based on 3D-Slicer, were applied on the validation set to measure the hematoma volume. The Digital Imaging and Communications in Medicine (DICOM) data of subjects meeting the selection criteria of the study were manually segmented by two experienced neurosurgeons. The hematoma segmentation model was built based on watershed algorithm and regional growth algorithm. Seed point selected by neurosurgeons was taken as the starting point of growth. Regional grayscale difference criterion combined with manual segmentation validation were adopted to determine the regional growth threshold that met the segmentation precision requirements for intracranial hematoma. Using manual segmentation as the gold standard, Bland-Altman consistency analysis was used to verify the consistency of the three other methods for measuring hematoma volume. Results: With manual segmentation as the gold standard, among the three methods of measuring hematoma volume, algorithm segmentation had the smallest percentage error, the narrowest range of difference, the highest intra-group correlation coefficient (0.987), good consistency, and the narrowest 95% limits of agreement ( LoA). The percentage error of its segmentation was not statistically significant for hematomas of different volumes. Conclusion: The segmentation method of spontaneous intracerebral hemorrhage based on watershed and regional growth algorithm shows stable measurement performance and good consistency with the clinical gold standard, which has considerable clinical significance, but it still needs further validation with more clinical samples.