Introduction: Breast cancer (BC) has been ranking first in incidence and the leading cause of death among female cancers worldwide based on the latest report. Regulated cell death (RCD) plays a significant role in tumor initiation and provides an important target of cancer treatment. Cuproptosis, a novel form of RCD, is ignited by mitochondrial stress, particularly the lipoylated mitochondrial enzymes aggregation. However, the role of cuproptosis-related genes (CRGs) in tumor generation and progression remains unclear.Methods: In this study, the mRNA expression data of CRGs in BC and normal breast tissue were extracted from TCGA database, and protein expression patterns of these CRGs were analyzed using UALCAN. The prognostic values of CRGs in BC were explored by using KaplanMeier plotter and Cox regression analysis. Genetic mutations profiles were evaluated using the cBioPortal database. Meanwhile, we utilized CIBERSORT and TIMER 2.0 database to perform the correlation analysis between CRGs and immune cell infiltration.Results: Our results indicated that CRGs expression is significantly different in BC and normal breast tissues. Then we found that upregulated PDHA1 expression was associated with worse endpoint of BC. Moreover, we also performed immune infiltration analysis of CRGs, and demonstrated that PDHA1 expression was closely related to the infiltration levels of CD4+ memory T cell, macrophage M0 and M1 cell and mast cell in BC.Conclusions: Our results demonstrated the prognostic and immunogenetic values of PDHA1 in BC. Therefore, PDHA1 can be an independent prognostic biomarker and potential target for immunotherapy of BC.

Background: The F-box and WD repeat domain containing (FBXW) family of SCF E3 complexes has 10 members that are responsible for ubiquitination and degradation of substrate proteins involved in cell cycle regulation and tumorigenesis. Among them, FBXW1 (also called b-TrCP1/BTRC) and FBXW7 are the central proteins in this category. However, there is still a lack of elaborate exploration of the contribution of FBXW family members, especially FBXW1 and FBXW7, in various tumor types.Methods: In this present study, we preliminarily analyzed the genetic structure characteristics of the FBXW family, and systematically investigated their expression patterns and clinical correlations based on the TCGA pan-cancer data. Survival analysis of FBXWs was also conducted through the Kaplan-Meier method. In addition, we assessed their immune infiltration level through immune-related algorithms like Timer and xCell.Results: There were obvious genetic heterogeneity and different clinical traits in FBXW family members. Moreover, we found that FBXW family genes may be useful in predicting prognosis and therapeutic efficacy using survival analysis. In addition, the immune infiltration of FBXW family was also clearly illustrated in this study. The results showed these genes were closely involved in immune components such as immune score, immune subtypes, tumor-infiltrating lymphocytes and immune checkpoints. Notedly, FBXW1 as an oncogene and FBXW7 as a tumor suppressor gene also show opposite relationships on immune cells.Conclusion: Our results provided valuable strategies to guide the therapeutic orientation concerning the role of FBXW family genes in cancer.

The blood flow velocity in the nailfold capillary is an important indicator of the status of microcirculation. The conventional manual processing method is both laborious and prone to human artifacts. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. Inspired by the Two-Stream Convolutional Networks, this study proposes an optical flow-assisted two-stream network to segment nailfold blood vessels. Firstly, we use U-Net as the spatial flow network and the dense optical flow as the temporal stream. The results show that the optical flow information can effectively improve the integrity of the segmentation of blood vessels. The overall accuracy is 94.01 %, the Dice score is 0.8099, the IoU score is 0.6806, and the VOE score is 0.3194. Secondly, The flow velocity of the segmented blood vessel is determined by constructing the spatial-temporal (ST) image. The blood flow velocity evaluated is consistent with the typical blood flow speed reported. This study proposes a novel two-stream network for blood vessel segmentation of nailfold capillary images. Combined with ST image and line detection method, it provides an effective workflow for measuring the blood flow velocity of nailfold capillaries.