Prostate cancer is the most common cancer in urology. While the genomic profiles and prognostic biomarkers of metastatic castration-resistant prostate cancer (mCRPC) have been well studied, the genomic characteristics of Chinese metastatic hormone-sensitive prostate cancer (mHSPC) and their association with clinical features remain incompletely characterized. This study aimed to characterize the genomic landscape of mHSPC in a Chinese cohort and explore correlations with clinical characteristics, with comparisons to data from the Stand Up to Cancer-Prostate Cancer Foundation (SU2C-PCF) cohort.

In this retrospective study, 52 patients diagnosed with mHSPC were enrolled, and tumor samples were subjected to targeted sequencing. Clinical, demographic, and pathological data were collected. The association between genomic alterations and clinical features was analyzed using Pearson's chi-squared test tests. Comparative analysis was performed using the SU2C-PCF cohort.

Compared to the SU2C-PCF cohort, Chinese patients with mHSPC had significantly higher mutation frequencies in FOXA1 (36.54% vs. 12.0%, P < 0.001), KDM6A (15.4% vs. 4.0%, P < 0.01), PIK3CA (15.4% vs. 4.7%, P = 0.01), and CTNNB1 (11.5% vs. 4.0%, P = 0.047), while AR mutations were significantly more frequent in the SU2C-PCF cohort (1.9% vs. 22.7%, P < 0.001). Homologous recombination repair gene mutations were detected in 36.54% of patients. PIK3CA mutations were associated with low PSA levels (50% vs. 11%, P = 0.044), TP53 mutations were significantly enriched in patients with high Gleason scores (43% vs. 0%, P < 0.001), and KDM6A (7% vs. 30%, P = 0.046) and SPOP (3% vs. 30%, P = 0.012) mutations were significantly enriched in patients with low Gleason scores.

Chinese patients with mHSPC have a distinct genomic landscape and show high genomic heterogeneity, TP53 mutation was significantly enriched in patients with mHSPC with high Gleason scores.

Not applicable. This study is a retrospective observational analysis.

Reproductive factors significantly impact the incidence of breast cancer (BC), a concerning trend. This study aims to reassess this association and provide recommendations for fertility policies to delay the onset of BC.

Utilizing data from a national survey, we applied weighted Cox and restricted cubic spline (RCS) methods to evaluate the impact of various modifiable and non-modifiable reproductive factors on breast cancer (BC) incidence. Interaction and subgroup analyses were conducted to investigate favorable fertility combinations and identify target populations.

Our analysis included 15,938 cases, representing 63,247,160 women in the United States, with 4.06% diagnosed as BC survivors. Multiple models consistently demonstrated that a later age at menarche (AM), higher parities, and an earlier age at first birth (AFB) were associated with a delayed BC onset. Specifically, women with four or more parities experienced a 32% reduction in BC risk (hazard ratio [HR] 0.68, 95% confidence interval [CI] 0.48-0.95), while those with an AFB of 25 years or older faced a 51% increased risk (HR 1.51, 95% CI 1.17-1.94). Women with an AM of 13 years or older had a 26% decreased risk in multivariate Cox analysis. Restricted cubic spline (RCS) analysis revealed a negative linear relationship between parities and BC risk. However, interaction and subgroup analyses indicated that these factors were less impactful than an earlier AFB, particularly in the high-risk group, including individuals with early AM or long-term survivors, where no significant differences were observed.

Among reproductive factors, both modifiable factors such as increased parity and earlier AFB, as well as the non-modifiable factor of later AM, delay the onset of BC. The effect of an earlier AFB in postponing BC onset are notably more pronounced compared to having more children, especially within specific high-risk cohorts.

The provision of specialized palliative care (SPC) and the timely discontinuation of cancer-targeted treatments (CTT) are increasingly considered important in cancer care at the end of life (EoL). EoL cancer care decisions are often initiated in the hospital, and little is known about associated expenditure in other parts of the health system. Our primary objective was to examine the total healthcare and care setting-specific expenditure associated with either exposure to SPC or timely discontinuation of CTT for patients with cancer in the last 4 weeks of life. Our secondary objectives were to (1) examine how these expenditures evolved in the last 4 weeks of life and across care settings and (2) explore the relation between the associated expenditures of SPC and timely discontinuation of CTT. Exposure to SPC was defined by the first successful referral to SPC within the 6- to 1-month period (i.e., last 4 weeks) before death. Timely discontinuation of CTT was defined as receiving no CTT within the last 4 weeks of life.

Using comprehensive linked Danish registry data, we conducted a nationwide matched cohort study (2011-2018), which analyzed care expenditure in various settings during the last 4 weeks of life for cancer patients, estimating costs with generalized linear model (GLM) and generalized estimating equation (GEE) models, and using logistic regression to assess SPC and timely discontinuation of CTT.

The total EoL care expenditure in the last 4 weeks of life was €3140 (96% CI €-3433 to €-2848) lower for patients exposed to SPC compared with non-exposed, mainly due to reduced hospital expenditure. Individuals exposed to timely discontinuation of CTT had €3430 (95% CI €-3649 to €-3211) lower expenditure per patient despite higher community, home-based, hospice, and primary care expenditure.

Our findings show the development of EoL care expenditure during cancer patients' final 4 weeks of life and can inform policymakers about the potential implications across the health system of changes in EoL care patterns.

Breast cancer is one of the most common cancer types in recent years. Early diagnosis of breast cancer increases the success of treatment. Breast cancer screening is thus highly critical. Mammography is a screening tool that is still globally valid. I would propose that Mammography can potentially be painful. In our study, we aimed to determine the level of pain experienced by using coolant spray before mammography imaging. Our study is a randomized double-blind prospective study. It was conducted on patients who agreed to participate in the study. In our study, coolant spray was applied to the case group and normal saline solution to the control group. After the procedure, data on experienced pain (using the VAS scale), comfort level, and the possibility of having a mammogram again were collected. The data obtained were analyzed in SPSS program. The number of patients admitted to the mammography unit during our study was 162. After applying the exclusion criteria, the study was completed with 136 patients. There was no statistical relationship between the placebo and coolant spray groups in terms of age, marital status, city/ provincial center-rural area of residence, educational status, employment status, smoking and menopausal status. The average VAS value experienced was lower in the coolant spray group than in the placebo group(4.82-6.84). The Coolant spray group had more positive responses to the question 'I would have mammography screening again'. Both findings were statistically significant(p < 0.05). A significant amount of pain is felt during the mammography procedure and this causes serious discomfort. Although different studies have been conducted to reduce this pain level, this is the first study in the literature to investigate the reduction of pain during mammography imaging using coolant spray. The pain perceived in the coolant spray group was considerably lower than in the placebo group. The rate of subsequent visits for mammography was similarly higher in the coolant spray group. A national screening program needs to involve the majority of the population in order to be feasible. For more screening to take place, appropriate environment and conditions should be provided. The most important reservation of the patients is the feeling of pain. Pain, which is one of the most important obstacles to mammography, can be reduced by using coolant spray.

MAPK14 and its related lncRNAs have been found to take part in the pathogenesis of cancers. We analyzed expression of MAPK14 and three related lncRNAs, namely NORAD, HCG11 and POLR1HASP (ZNRD1ASP) in 42 pairs of lung tumors and their nearby normal samples. ZNRD1ASP was the mostly up-regulated gene in the tumors in the current study with the expression ratio (95% CI) of 10.46 (3.1-35.4). NORAD, HCG11 and MAPK14 were the other up-regulated genes in order with expression ratios (95% CIs) of 9.57 (2.99-30.6), 5.11 (1.65-15.8) and 4.9 (1.92-12.6), respectively. ROC curve analyses revealed that ZNRD1ASP had the highest accuracy and AUC values among the studied genes. However, NORAD with the sensitivity of 0.78 and negative predictive value of 70% performed better than the other transcripts in these two parameters. Taken together, MAPK14 and its related lncRNAs may have important roles in the pathogenesis of lung cancer.

Cervical cancer is the second most common cancer among women worldwide, causing nearly half of all diagnosed cases to result in death. Most of the cases, women are dying due to ignorance and lack of adequate knowledge, which is completely unacceptable. This study aimed to assess the effects of health educational intervention on knowledge and perception of cervical cancer and its screening among women in Tangail District of Bangladesh. This was a quasi-experimental (pre-post) study conducted in Tangail District, Bangladesh. Baseline data were collected using a pre-validated questionnaire from 400 rural women, followed by a health education intervention. After one month, a post-intervention survey using the same questionnaire was conducted to assess changes in knowledge and perception. Data were analyzed using descriptive statistics, chi-square test, McNemar's test, and Friedman test to evaluate pre-post differences and associations with socio-demographic variables. All statistical tests had been conducted using Microsoft Excel (Version 2024) and SPSS (version 27). Out of 400 participants, the majority (44.8%) was aged between 25 and 34 years and most (96.8%) of the women were housewives. A significant improvement was observed in knowledge about cervical cancer and its screening after the health education intervention. Before the intervention, 69.5% of the participants were aware of cervical cancer but after the intervention it increased to 100% (p < 0.001). Additionally, the awareness about cervical cancer screening rises from 20.3% to 100% (p < 0.001). Moreover, before the intervention, only 14.8% of participants were aware of the HPV vaccine, while this figure increased to 98.3% (p < 0.001). Furthermore, after the intervention the percentage of participants receiving cervical cancer screenings increased from 4.3% to 9.8% (p = 0.004). The study observed that health education intervention programs significantly increased women knowledge and perception regarding cervical cancer and its screening. Cervical cancer mortality rate can be highly reduced if it can be detected early and take proper measures.

Liquid-liquid phase separation (LLPS) has been linked to the initiation and progression of cancers. This study aimed to investigate the LLPS-related molecular features and develop a prognostic signature for hepatocellular carcinoma (HCC). Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data were obtained from open-source repositories. The Seurat package was used for processing and analyzing scRNA-seq data. LLPS-related genes (LLPSRGs) were identified by integrating scRNA-seq and bulk RNA-seq data. Univariate Cox and LASSO regression analyses were employed to construct an LLPS-related prognostic risk model. A predictive nomogram was also developed based on the risk score and clinicopathologic features. Additionally, correlations between clinicopathological features, biological functions, and tumor mutations with prognostic risk were explored. Experimental validation included cell transfection, wound healing, and transwell assays. Fourteen distinct cell clusters were identified, with malignant hepatocytes exhibiting the highest LLPS score. High-LLPS hepatocytes strongly interacted with other cells, showing elevated expression of EGFR-ERGF, EGFR-AREG, MIF-CD44, and MIF-CXCR4 interactions. The LLPS score was associated with malignant differentiation of hepatocytes. Ten LLPSRGs were identified as part of the LLPS-related risk signature, and potential agents were predicted: Olitigaltin and Lactose for LGALS3, and Sitamaquine, Phenazopyridine, Sulfanilamide, Pamaquine, Sodium ascorbate, and Co-trimoxazole for G6PD. Knockdown of LGALS3 inhibited HCC cell migration and invasion. LLPS-related molecular characteristics were identified, and an LLPS-related prognostic model was developed, offering novel insights into HCC research and providing potential therapeutic targets.

Breast cancer is the most common cancer among women. However, individuals with a lower socioeconomic status (SES) tend to experience poorer prognosis. We aimed to study disparities in diagnosis, treatment, quality of care, and survival by SES in Iran. Data from breast cancer patients at the Cancer Institute of Iran between 2014 and 2016, registered in the clinical breast cancer registry of Iran (CBCR-IR), were used. Clinical data were obtained from CBCR-IR, while follow-up data and information on SES, including 10 household assets and education level, were gathered through telephone interviews. The primary outcome was survival, determined through active follow-up. Additionally, we studied delays in treatment and the quality of care based on European Society of Breast Cancer Specialists (EUSOMA) standards. A Cox proportional hazard model was used to investigate the association between SES and survival in women with breast cancer. The inequality in breast cancer survival was analyzed using the Blinder-Oaxaca decomposition model. The 5-year overall survival rates were 87.7% (95% CI: 83.9-90.7) for low SES patients, 90.2% (95% CI: 86.7-92.8) for mid SES, and 92.1% (95% CI: 89.0-94.3) for high SES patients (log-rank test P-value = 0.077). A 3.3% gap in breast cancer mortality was observed between high and low SES patients (95% CI: 1.3-5.5), with the model explaining 2.2% of this difference. Differences in delay in treatment and stage at diagnosis accounted for 0.5% (95% CI: 0.2-0.7) and 1.8% (95% CI: 1.2-2.3) of the disparity. Breast cancer mortality is higher in low SES patients, primarily due to late diagnosis and delays in treatment initiation.

Soft Tissue sarcomas (STS) are a group of heterogeneous and complex diseases where being able to predict the appearance of metastases is key to inform clinical decisions, especially the prescription of adjuvant chemotherapy. We developed SarcNet: a multimodal deep-learning algorithm based on histological whole-slides and clinical variables to predict metastatic relapse in patients affected with limbs and trunk wall STS. Two independent series were investigated simultaneously in a privacy-enhanced multicentric setup using Federated Learning: Centre Léon Bérard (n = 221) and Institut Bergonié (n = 390). We then collected two additional validation cohorts from Centre Léon Bérard (n = 93) and Institut Bergonié (n = 124) to validate the performance of the trained algorithm. Evaluated in cross-validation setting on the first two cohorts, SarcNet achieved a performance of 0.797 AUC (Area Under the Receiver-operating characteristic Curve, 0.762-0.833) to predict 5-year MFS comparable to the Sarculator, current state-of-the-art nomogram (0.778 (0.743-0.814)), and outperforming the FNCLCC grading, widely used in practice (0.706 (0.670-0.743), P-value < 0.001). On validation cohorts, SarcNet performance is still on-par to the Sarculator and FNCLCC rating. Interpretability investigations of SarcNet highlighted histological patterns driving the prediction of metastatic relapse such as atypia, tumor cellularity and anisokaryosis. This model may assist clinical decisions by identifying high-risk patients that could benefit from neoadjuvant or adjuvant treatment.

Accurate preoperative glioma grading remains a critical challenge in neuro-oncology. This study presents a novel integrated approach combining deep learning architectures with radiomics features derived from multi-parametric MRI to improve preoperative glioma grading accuracy. In this retrospective multi-center study, we analyzed 847 patients with histopathologically confirmed gliomas from 5 tertiary neurosurgical centers. Multi-parametric MRI sequences (T1, T1-contrast, T2, FLAIR) were processed using a dual-stream framework where: (1) a 3D convolutional neural network extracted deep imaging features, and (2) 1,423 quantitative radiomic features were extracted and selected using a recursive feature elimination algorithm. We developed an ensemble model that integrates both feature streams with clinical variables. Model performance was evaluated through 5-fold cross-validation and external validation on an independent cohort (n = 213). The integrated model achieved superior performance (AUC = 0.946, 95% CI: 0.927-0.965) compared to radiomics-only (AUC = 0.891) or deep learning-only (AUC = 0.903) approaches for distinguishing high-grade (WHO grades III-IV) from low-grade (WHO grades I-II) gliomas. Notably, the model demonstrated robust performance across different MRI acquisition parameters (AUC = 0.921 on external validation). Subgroup analysis revealed particular efficacy in identifying isocitrate dehydrogenase (IDH) wild-type gliomas (sensitivity 0.954, specificity 0.912). The model accurately identified 89.2% of gliomas with molecular features associated with aggressive behavior but ambiguous conventional imaging characteristics. This integrated radiomics-deep learning approach significantly improves preoperative glioma grading accuracy across diverse patient populations and imaging protocols. The proposed framework offers a non-invasive tool for preoperative risk stratification, potentially informing surgical planning and treatment strategies. The model's interpretability provides insights into imaging biomarkers associated with glioma aggressiveness.