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Promoting Empowerment: "Give Myself Your current Hand" - As their Hand/What Target?
A novel SS18-POU5F1 fusion gene was recently reported in soft tissue sarcoma occurring in three adolescent and young adult patients. Herein, we firstly reported the treatment response of SS18-POU5F1 sarcoma to immune checkpoint inhibitor, angiogenesis inhibitor, chemotherapy and radiotherapy. Our patient demonstrated no response to various systemic therapies including immune checkpoint inhibitor, angiogenesis inhibitor and chemotherapy. However, we noted that the SS18-POU5F1 sarcoma had a quick, robust but transient clinical response to radiotherapy. Further studies are needed to elucidate the mechanism underlying the different tumor response to radiotherapy and systemic therapy in this kind of tumor.Transcription factors (TFs) are the mainstay of cancer and have a widely reported influence on the initiation, progression, invasion, metastasis, and therapy resistance of cancer. However, the prognostic values of TFs in breast cancer (BC) remained unknown. In this study, comprehensive bioinformatics analysis was conducted with data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We constructed the co-expression network of all TFs and linked it to clinicopathological data. Differentially expressed TFs were obtained from BC RNA-seq data in TCGA database. The prognostic TFs used to construct the risk model for progression free interval (PFI) were identified by Cox regression analyses, and the PFI was analyzed by the Kaplan-Meier method. A receiver operating characteristic (ROC) curve and clinical variables stratification analysis were used to detect the accuracy of the prognostic model. Additionally, we performed functional enrichment analysis by analyzing the differential expressed gene between high-risk and low-risk group. A total of nine co-expression modules were identified. The prognostic index based on 4 TFs (NR3C2, ZNF652, EGR3, and ARNT2) indicated that the PFI was significantly shorter in the high-risk group than their low-risk counterpart (p less then 0.001). The ROC curve for PFI exhibited acceptable predictive accuracy, with an area under the curve value of 0.705 and 0.730. In the stratification analyses, the risk score index is an independent prognostic variable for PFI. Functional enrichment analyses showed that high-risk group was positively correlated with mTORC1 signaling pathway. In conclusion, the TF-related signature for PFI constructed in this study can independently predict the prognosis of BC patients and provide a deeper understanding of the potential biological mechanism of TFs in BC.
The influence of surgical approaches [including mastectomy, breast-conserving therapy (BCT) and post-mastectomy breast reconstruction (PMBR) on prognosis of young women (<40 years old) with operable breast cancer has not been determined yet, and this might vary in patients with different marital statuses. Therefore, we aimed to investigate the effect of surgery on survival outcomes for young women with operable breast cancer in different marital statuses.

We used the Surveillance, Epidemiology, and End Results (SEER) database to identify young women with operable breast cancer between 2004 and 2016, who underwent mastectomy, BCT or PMBR. We assessed overall survival (OS) and breast cancer-specific survival (BCSS) using the Kaplan-Meier method and hazard ratios using multivariate Cox proportional hazard regression.

Compared to mastectomy, both of BCT and PMBR conferred better OS (BCT HR = 0.79, 95%CI 0.69-0.90, p <0.001; PMBR HR = 0.70, 95%CI 0.63-0.78, p <0.001) and BCSS (BCT HR = 0.79, 95%CI 0.69-0.91, p = 0.001; PMBR HR = 0.73, 95%CI 0.65-0.81, p <0.001), but there was no significant difference of survival between BCT and PMBR group. The survival benefit of BCT compared to mastectomy remained significant in unmarried young women (OS HR = 0.68, 95%CI 0.55-0.83, p <0.001; BCSS HR = 0.69, 95%CI 0.56-0.86, p = 0.001) but not in the married (OS HR = 0.89, 95%CI 0.75-1.05, p = 0.177; BCSS HR = 0.89, 95%CI 0.75-1.05, p = 0.161), while no matter married or not, PMBR group had better OS and BCSS than mastectomy group but not BCT group.

Both of BCT and PMBR had improved survival compared to mastectomy for young women with operable breast cancer. The survival benefit of BCT compared to mastectomy remained significant in unmarried patients but not in married patients.
Both of BCT and PMBR had improved survival compared to mastectomy for young women with operable breast cancer. The survival benefit of BCT compared to mastectomy remained significant in unmarried patients but not in married patients.Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. CX-3543 purchase With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.
Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.

A comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan-Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.
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