Suboptimal adherence to chemotherapy regimens remains a significant challenge in cancer management, adversely impacting treatment outcomes and quality of life. This study aimed to develop and evaluate a user-centric mobile app intervention to promote adherence and enhance quality of life among cancer patients undergoing chemotherapy.

This meta-analysis investigates the association between the p53 rs1042522 polymorphism and thyroid cancer, analyzing 18 case-control studies with 2,116 cases and 4,017 controls. It finds that the C allele is linked to a higher cancer risk (OR 1.572, 95% CI: 1.062-2.326, p = 0.024) but shows significant heterogeneity (I = 94.15%). Subgroup analyses indicate protective effects in Caucasian, Asian, and mixed populations (ORs around 0.008 to 0.011). Notably, Follicular and Differentiated Thyroid Carcinomas show strong protective associations, suggesting that while the C allele may increase risk, certain populations may experience reduced risk, emphasizing genetic complexity.

Breast Cancer (BC) is a leading cancer among women, influenced by genetic polymorphisms. This meta-analysis examines (rs4244285) and (rs2234693) polymorphisms and BC susceptibility.

To prolong patients' survival has become a major topic in cancer research field. Albumin (Alb) to fibrinogen (Fib) ratio(AFR) or its reciprocal FAR, Fib to pre-albumin(PA) ratio(FPR) or its reciprocal PFR have been found significantly associated with survival of cancer patients and can be used as prognosis predictors for human cancers. Here, we summarized the emerging knowledge regarding the roles of AFR, FAR, FPR and/or PFR played in predicting prognosis for digestive system cancers.

Immune checkpoint inhibitors (ICIs) are a novel and promising anti-cancer therapy. We conducted this systematic review to precisely quantify the occurrence and development for actue kidney injury(AKI) following ICIs treatment for cancer. We conducted a search of the PubMed, Embase, Web of Science, and Cochrane Library databases. Twenty-nine studies, comprising 24,953 cancer patients who received ICIs were finally eligible. The incidence of AKI was 16.2% (95%CI:12.8%-19.8%); the incidence of immune checkpoint inhibitor-associated acute kidney injury (ICPi-AKI) was 3.1%(95%CI:2.4%-4%); the incidence of non-ICPi-AKI was 11.2%(95%CI:8.4%-14.3%), and the incidence of sustained AKI was 14.9%(95%CI:7.5%-24.3%). Patients who developed AKI (HR = 1.521(95%CI:1.208-1.916)) and ICPi-AKI (HR = 1.407(95%CI:1.059-1.869)) exhibited an elevated risk of all-cause mortality. An increased risk for AKI was observed with preexisting chronic kidney disease (CKD) and combined with other extrarenal immune-related adverse events (irAEs). The use of nonsteroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitor (PPI), diuretic, renin-angiotensin-aldosterone system (RAASi), antibiotics and fluidone was also significantly associated with incident AKI. Combined therapy had a greater impact on renal injury compared to monotherapy. Patients using ipilimumab were more prone to developing AKI, compared to those using nivoluma. CTLA4 (ref'PD-1) was associated with a higher likelihood of sustained AKI. The use of PDL-1(ref='PD-1) was linked to an increased susceptibility to ICPi-AKI. The occurrence of AKI was intricately linked to specific complications, the concomitant use of certain medications, and the specific regimen of ICIs. This deserves our attention.

Although cancer stem cells (CSCs) from pancreatic carcinoma were first identified many years ago, their properties and functions remain unclear. In addition, characterizing these cells may provide insight into potential accurate targeting therapies. The aim of the present study was to investigate the characterization of CD44CD24 pancreatic CSCs and the effect on the growth and tumorigenesis. CD44CD24 pancreatic CSCs were isolated from PANC-1 cell lice. It was observed that CD44CD24 cells were stronger than control cells in the fields of proliferation, sphere-forming ability, invasion and migration, colony formation, and that expression of Notch-1 mRNA and protein were higher in CD44CD24 cells than in the control cells. This study suggested that CD44CD24 cells exhibited cancer cell stemness , and promoted tumorigenesis and invasion of pancreatic cancer which is important to the development of accurate therapies targeting pancreatic CSCs. It is concluded that this study provided most important theoretical foundation for the clinical research and will be applied to individuals with pancreatic cancer by the method of clinical translational studies in the future.

To describe family involvement in decision making for treatment in China.

Recently some serologic parameters emerged as potential prognostic factors of triple-negative breast cancer (TNBC). We aimed to establish the most relevant factors and select optimal cutoff points for prospective investigations. Data from 137 TNBC patients treated with neoadjuvant chemotherapy were analyzed. Beyond pathological factors, white blood cell, neutrophil (NE), lymphocyte (LY) and platelet counts, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII) were investigated at baseline and before the third cycle. In univariate analysis, most parameters at baseline (NE1, LY1, NLR1, PLR1, SII1), in multivariate analysis NLR1 and pathological stage showed significant association with survival.

Nasopharyngeal carcinoma (NPC) is one of the most common malignant head and neck tumors. Despite being sensitive to radiation, the prognosis of some NPC patients remains unoptimistic due to the resistance to therapeutic approaches. Ubiquitination is a crucial post-translational modification of proteins, and is key to maintaining protein homeostasis . Studies have shown that ubiquitination modification can regulate cell proliferation, apoptosis, and other processes, thereby affecting the development of NPC. In this review, we summarize the research on ubiquitination regulation in NPC, aiming to explore new therapeutic targets and provide feasible solutions to improve the prognosis of NPC patients.

To screen predictors of prognosis in NMIBC patients treated with RC and construct a predictive model that accurately assesses their overall survival (OS). 1129 patients screened from the SEER database were randomized in a 7:3 ratio into a training group (790) and a validation group (339). Univariate and multivariate Cox regression analyses screened for prognostic factors, the best predictive model was determined by AIC minimum, and variables were examined for multicollinearity. Based on the total score obtained from the column line graph and the X-Tile procedure to find the best cutoff point and create a risk classification system. Finally, the model was evaluated and validated by C-index, AUC, drawing calibration curves (1000 bootstrap resamples), and Decision curve analysis (DCA). In the training group, the C-index was 0.67, and the ROC curves showed AUCs of 0.655, 0.704, and 0.722 for 1-, 5-, and 10-year OS, respectively; in the validation group, the AUCs were 0.738, 0.694, and 0.705, respectively. Meanwhile, the predictive performance of the present model was superior to that of the TNM staging, pLNR, and N staging, which can be used as a basis for patient counseling, follow-up scheduling, and treatment choice The model can provide a basis for patient counseling, follow-up scheduling and treatment selection.

The goal of this study was to look at the long-term survival outcomes and clinical characteristics of stage II/III locally advanced rectal cancer (LARC) patients who acquired pathological complete response (pCR) following neoadjuvant chemoradiotherapy (NCRT). The clinicopathological characteristics and treatment details of 277 LARC patients with pCR, relapse-free survival (RFS), overall survival (OS), and locoregional and systemic recurrence rates, were assessed. The 5-year RFS and OS rates were 85.6% and 90.9%. The rates of local and systemic recurrence were 3.6% and 7.9%. Our study confirmed the favorable results in survival in patients with LARC who achieved pCR.

Mammary Paget's disease (MPD) is a rare breast malignancy often associated with ductal carcinoma in situ or invasive carcinoma. However, its diagnosis remains challenging owing to the subtlety or absence of findings on conventional imaging. In this study, we retrospectively analyzed 12 Japanese patients with MPD. All patients showed uniform overexpression of the human epidermal growth factor receptor 2 (HER2; immunohistochemistry score = 3+), with 92% exhibiting associated ductal carcinoma in situ. Magnetic resonance imaging (MRI) revealed skin and nipple enhancement in 78% of patients, along with non-mass enhancement and nipple thickening that correlated with the pathological findings. Moreover, Ki-67 proliferation index was high in most cases (median, 67%), indicating the presence of biologically active tumors. No recurrence or death was observed during the median follow-up period of 96 months. Overall, our findings suggest that HER2-positive MPD exhibits aggressive biological behaviors despite a subtle clinical presentation and highlight the importance of MRI in its detection. Furthermore, integration of imaging with pathological and molecular marker assessment is essential for accurate MPD diagnosis and treatment. This study on a Japanese cohort provides valuable insights and highlights the diagnostic utility of MRI for MPD, especially HER2-driven MPD.

Cervical cancer is the fourth most frequent cancer in females, with a high mortality rate globally. Persistent infection with high-risk, oncogenic human papillomavirus (HPV) types is a critical etiologic factor in the progression of the disease. Unfortunately, cervical cancer often remains undiagnosed until advanced stages, hence limiting treatment effectiveness. Therefore, identifying precise and significant biomarkers is crucial. High-throughput sequencing technologies have revolutionized targeted cancer therapy research by generating extensive data for analysis. This study employed bioinformatics and machine learning (ML) approaches to identify dysregulated genes with significant diagnostic value in cervical cancer, utilizing transcriptomics datasets. Seven potential diagnostic biomarker genes (, , , , , , and ) were validated by a real-time polymerase chain reaction (RT-PCR) experiment. The ML models were developed using significantly differentially expressed genes (DEGs) involved in important pathways for cervical cancer. ML prediction models are available at https://github.com/PGlab-NIPER/CC_Pred.

Colorectal cancer is a leading cause of morbidity and mortality worldwide. This study investigates the association between GLP-1 receptor agonists (GLP-1 RAs) and five-year mortality in patients with primary colon cancer, considering BMI. Using data from the University of California Health Data Warehouse, 6,871 patients were analyzed. Five-year mortality was 15.5% for GLP-1 RA users compared to 37.1% for non-users. Analyses showed significantly lower odds of five-year mortality with GLP-1 RA use (OR = 0.38, 95% CI: 0.21-0.64). This benefit persisted after adjusting for confounders, including disease severity, but was found to only extend to high obese patients (BMI > 35) in stratified modeling.

Breast cancer primarily affects women, caused due to the excess growth of malignant breast tissues. The segmentation and early detection process suffered due to the complex and varied nature of breast tissue. To address this challenge, this research proposes a Convolutional Neural Network model with Contextual Relationship Embedding to accurately segment pathological mass regions in mammogram images. In this research work, the mammogram images are collected from datasets and are preprocessed to enhance image quality, noise reduction and contrast enhancement. By using a Deep Convolutional Neural Network, the edges in the highly contrasted regions, complex structure and spatial relationships of the images are gathered by using different operators. The extracted features are concatenated through the Fully Connected-Convolutional Block Attention Module. The contextual relationship embedded features are integrated with the original features, guided by the cross-entropy loss function with contextual relationship constraints. This enables the model to generate more precise decisions for segmentation and boundary identification. The proposed method's efficiency is validated and the proposed model achieves superior performance with an accuracy of 99.59% and an error rate of 0.405%. Overall, this research article concludes that the proposed model is more efficient for breast cancer detection than other existing models.

Lung cancer (LC) is one of the major causes of death worldwide. Early diagnosis helps to improve the patient survival outcome. The surgeon makes use of Computed Tomography (CT) for detecting LC using the aid of a Computer-Aided Diagnosis (CAD) system to identify LC effectively, but it has issues related to processing time and diagnostic precision that continue to pose significant challenges. To address this, a Deep Residual Xception Network (DRX-Net) approach has been introduced for identifying the LC. Initially, the CT image is obtained and then denoising is performed using a Wiener filter. Subsequently, the segmentation of lung nodule is conducted using Pyramidal Attention-based Y Net (PAY-Net), which uses a hybrid loss function combining Binary Cross Entropy, Tanimoto Similarity, and Dice Loss. The segmented image undergoes data augmentation followed by feature extraction. For LC detection, the selected features are processed using DRX-Net, which merges the Xception with a Deep Residual Network (DRN). Furthermore, the results show that the proposed DRX-Net achieved an accuracy of 93.988%, a True Positive Rate (TPR) of 95.567%, and a True Negative Rate (TNR) of 91.432% when evaluated using a K Group of 8.

Metastatic melanoma carries a poor prognosis. Immune checkpoint inhibitors (ICIs) have improved outcomes, but responses remain variable, highlighting the need for simple prognostic biomarkers. Inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lactate dehydrogenase (LDH) reflect tumour burden and inflammation, though their clinical utility is unstandardised.

As use of real-world evidence (RWE) in oncology continues to increase, guidance is needed to ensure the patient voice is captured when generating RWE. This paper proposes a practical methodological framework for patient-centered RWE (PCRWE) throughout oncology product development. The need for a novel framework was first established by a review of existing literature and RWE guidelines. This review indicated an unmet need for a clear definition of PCRWE and a framework to guide PCRWE research in oncology. We define PCRWE as RWE that incorporates patient-centered objectives, provides insights into patient-relevant questions, and may lead to assessments of the usage, benefits, or risks of a medical treatment reflecting the patient perspective. The review's findings were used to create a preliminary PCRWE framework, which was finalized following interviews with RWE stakeholders and oncologists. The final PCRWE framework, which is grounded in the existing regulatory and scientific landscape, is an interactive visual tool for generating, implementing, and disseminating PCRWE in oncology. It accommodates various levels of expertise among users and supports the alignment of terminology to describe PCRWE. The framework will enable stakeholders to identify unmet needs from the patient perspective and to more effectively demonstrate the value of new oncology products.

Parathyroid carcinoma, a rare endocrine malignancy, is a significant diagnostic and therapeutic challenge due to its overlapping features with benign parathyroid diseases and high recurrence rates.

The primary objective of this review is to provide a comprehensive analysis of salivary biomarkers in the context of oral cancer, with a particular focus on oral squamous cell carcinoma. Oral cancer is a serious global health concern, ranking as the sixth most common cancer worldwide with over 300,000 new cases annually, and as the third most prevalent cancer in India. Its high morbidity and mortality are largely attributed to late-stage diagnosis and limited access to timely care. The current diagnostic gold standard, tissue biopsy, is invasive, costly, and unsuitable for population-level screening, creating a need for alternative approaches. This review critically evaluates recent advancements in diagnostic methodologies, emphasizing saliva as a noninvasive diagnostic medium. It examines relevant clinical case studies to demonstrate the diagnostic efficacy of salivary biomarkers and explores key etiological factors associated with oral cancer. Public health strategies initiated by governmental agencies to improve early detection, screening, and awareness are also discussed. The findings highlight that salivary biomarkers hold significant promise for early detection and cancer diagnostics. Conclusions emphasize the translational gaps that persist in this area, underscoring the need for further research to enable their integration into diagnostic protocols, screening programs, and public health initiatives.