Wei Zhao

Mitochondrial activity related genes of mast cells identify poor prognosis and metastasis of ovarian cancer

The pro-tumorigenic or anti-tumorigenic role of tumor infiltrating mast cells (TIMs) in tumors depends not only on the type of cancer and the degree of tumor progression, but also on their location in the tumor bulk. In our investigation, we employed immunohistochemistry to reveal that the mast cells (MCs) in the tumor stroma are positively correlated with metastasis of ovarian cancer (OC), but not in the tumor parenchyma. To delve deeper into the influence of different culture matrix stiffness on MCs' biological functions within the tumor parenchymal and stromal regions, we conducted a transcriptome analysis of the mouse MC line (P815) cultured in two-dimensional (2D) or three-dimensional (3D) culture system. Further research has found that the softer 3D extracellular matrix stiffness could improve the mitochondrial activity of MCs to promote proliferation by increasing the expression levels of mitochondrial activity-related genes, namely Pet100, atp5md, and Cox7a2. Furthermore, employing LASSO regression analysis, we identified that Pet100 and Cox7a2 were closely associated with the prognosis of OC patients. These two genes were subsequently employed to construct a risk score model, which revealed that the high-risk group model as one of the prognostic factors for OC patients. Additionally, the XCell algorithm analysis showed that the high-risk group displayed a broader spectrum of immune cell infiltrations. Our research revealed that TIMs in the tumor stroma could promote the metastasis of OC, and mitochondrial activity-related proteins Pet100/Cox7a2 can serve as biomarkers for prognostic evaluation of OC.

A feasibility study of dose-band prediction in radiation therapy: Predicting a spectrum of plan dose

The current deep learning-based dose prediction methods only predict one dose distribution. If the predicted dose is inaccurate, no additional options can be selected. To overcome this limitation, we propose a novel dose prediction method called "dose-band prediction," which provides a spectrum of predicted dose distributions for planning and quality assurance (QA) purposes. We utilized Upper/Lower-band losses in 3D neural networks to establish the Upper/Lower-band models (UBM/LBM). The maximum/minimum rational dose predicted in UBM/LBM defined the ideal dose spectrum for each voxel. We enrolled 104 nasopharyngeal carcinoma cases with tomotherapy (dataset 1), 54 cervical carcinoma cases with IMRT (dataset 2), and 37 cervical carcinoma cases with VMAT (dataset 3) in the study. Moreover, a dose band-based auto planning (Auto-plan The UBM/LBM doses tend to be higher/lower than the clinical dose, forming a predicted dose spectrum. The Middle-line dose represents the average of the Upper/Lower-band, which was consistent with the clinical dose. The mean differences of the planning target volumes (PTVs) and organs at risk (OARs) for the Upper-band, Middle-line, and Lower-band in Dataset 1 were 3.66 %, -0.40 %, and -4.48 % in Dataset 2, they were 2.40 %, -1.62 %, and -5.57 %; in Dataset 3, they were 2.18 %, -0.59 %, and -3.31 %. When PTVs meet prescription, the mean difference between Auto-plan The dose-band prediction successfully predicted a spectrum of doses, making auto-planning and QA flexible and high quality.