Aim This research aimed to explore the level of hope and symptom burden of breast cancer women undergoing chemotherapy, and predictive factors of hope were also investigated. Background Chemotherapy brings physical and psychological stress to breast cancer patients. As an effective coping strategy, hope gives them the courage to overcome difficulties and improve prognosis and survival. Therefore, efforts are needed to raise hope. Design/Methods A total of 450 women who were undergoing breast cancer chemotherapy participated in this cross‐sectional study. Sociodemographic data, disease characteristics, and measures of hope and symptom burden were collected using questionnaires. Hope was assessed using the validated Herth Hope Index, and the previously validated Memorial Symptom Assessment Scale was used to assess symptom burden. This paper adhered to the STROBE guidelines. Results Chinese breast cancer chemotherapy women hope average scores of 30.15 ± 4.82 were in the medium range of the Hearth Hope Index as specified by Herth to be 24–35. Patients with age ≤45, religious beliefs and lighter symptom burden have a higher level of hope. These variables explained a total of 22.9% of the variation in hope. Conclusions The level of hope for women undergoing breast cancer chemotherapy still needs to be further improved. Symptom burden can negatively predict hope. Relevance to clinical practice If nurses can decrease breast cancer chemotherapy women symptom burden, there is an impact on increasing levels of hope.
We explore the association of hope and quality of life in breast cancer chemotherapy women. Their quality of life is related to treatment effects and disease outcomes. This cross-sectional study was conducted in City, China, in 2017. In a convenience sampling, 450 women who underwent breast cancer chemotherapy were selected from two hospitals. Descriptive statistics, single-factor analysis, Spearman correlation, linear regression, and structural equation modeling were used to analyze data. The mean quality of life score was 65.65. In linear regression analysis, we found patients’ quality of life was significantly related to age, marital status, education level, chemotherapy cycle, and hope. Structural equation results showed the “temporality and future” and “interconnectedness” subscales of the HHI explained 43% of the variance in quality of life. We found hope is an important aspect in quality of life, and further research is needed to determine if nurses can influence this aspect of care.
Background Necroptosis is a novel type of programmed cell death distinct from apoptosis. However, the role of necroptosis in ovarian cancer (OC) remains unclear. The present study investigated the prognostic value of necroptosis-related genes (NRGs) and the immune landscape in OC. Methods The gene expression profiling and clinical information were downloaded from the TCGA and GTEx databases. Differentially expressed NRGs (DE-NRGs) between OC and normal tissueswere identified. The regression analyses were conducted to screen the prognostic NRGs and construct the predictive risk model. Patients were then divided into high- and low-risk groups, and the GO and KEGG analyses were performed to explore bioinformatics functions between the two groups. Subsequently, the risk level and immune status correlations were assessed through the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden (TMB) and the drug sensitivity were also analyzed based on the two-NRG signature in OC. Results Totally 42 DE-NRGs were identified in OC. The regression analyses screened out two NRGs (MAPK10 and STAT4) with prognostic values for overall survival. The ROC curve showed a better predictive ability in five-year OS using the risk score. Immune-related functions were significantly enriched in the high- and low-risk group. Macrophages M1, T cells CD4 memory activated, T cells CD8, and T cells regulatory infiltration immune cells were associated with the low-risk score. The lower tumor microenvironment score was demonstrated in the high-risk group. Patients with lower TMB in the low-risk group showed a better prognosis, and a lower TIDE score suggested a better immune checkpoint inhibitor response in the high-risk group. Besides, cisplatin and paclitaxel were found to be more sensitive in the low-risk group. Conclusions MAPK10 and STAT4 can be important prognosis factors in OC, and the two-gene signature performs well in predicting survival outcomes. Our study provided novel ways of OC prognosis estimation and potential treatment strategy.
Background: Necroptosis is a novel type of programmed cell death distinct from apoptosis. However, the role of necroptosis in ovarian cancer (OC) remains unclear. The present study tried to investigate the prognostic value of necroptosis‐related genes (NRGs) and the immune landscape in OC. Methods: The gene expression profiling and clinical information were downloaded from the TCGA and GTEx databases. Differentially expressed NRGs (DE-NRGs)were identified. The regression analyses were conducted to screen the prognostic NRGs and construct the predictive risk model. Patients were then divided into high- and low-risk groups, and the GO and KEGG analyses were performed to explore bioinformatics functions between the two groups. Subsequently, the risk level and immune status correlations were assessed through the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden (TMB)and the drug sensitivity were also analyzed based on the two-NRG signature in OC. Results: Totally 42 DE-NRGs were identified in OC. The regression analyses screened out two NRGs (MAPK10 and STAT4) with prognostic values for overall survival. The ROC curve showed a better predictive ability in five-year OS using the risk score. Immune-related functions were significantly enriched in the high- and low-risk group. Macrophages M1, T cells CD4 memory activated, T cells CD8 and T cells regulatory infiltration immune cells were associated with the low-risk score. The lower tumor microenvironment score was demonstrated in the high-risk group. Patients with lower TMB in the low-risk group showed a better prognosis, and a lower TIDE score suggested a better immune checkpoint inhibitor response in the high-risk group. Besides, cisplatin and paclitaxel were found more sensitive in the low-risk group. Conclusions: MAPK10 and STAT4 can be important prognosis factors in OC, and the two-gene signature performs well in predicting survival outcomes. Our study provided novel ways of OC prognosis estimation and potential treatment strategy.
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