Cancer Investigation最新文献

Objective: The ExPRO (External factors influencing patient reported outcomes of patients with malignant diseases) study explored associations between QoL data and environmental factors on the day of questionnaire completion: mean temperature, sunshine hours, season, and lunar phase.

Methods: We undertook a cross-sectional analysis of baseline data in the prospective cohort study at two cancer centers in eastern Germany. From December 2020 to December 2021, cancer patients completed the EORTC QLQ-C30 questionnaire upon admission. Statistical analysis was performed to explore associations between QoL data and environmental factors, including temperature, sunshine hours, season, and lunar phases.

Results: We received 5040 responses (54% male). QoL scores were highest at 25-30 °C and lowest at 5-10 °C (mean 61.3 vs. 52.6, p <0.001). Insomnia was highest at ≤0 °C and lowest at 25-30 °C (mean 39.3 vs. 29.5, p <0.001). QoL was highest with 8 hours of sunshine and lowest with 0 hours (mean 56.9 vs. 50.9, p = 0.003).

Conclusion: Higher temperatures, more sunshine, and summer seasons are associated with higher QoL in cancer patients, while lower temperatures and reduced sunlight are associated with poorer QoL. These findings highlight the need to consider environmental factors in PRO assessments.

Apoptosis, or programmed cell death, is a fundamental biological process essential for maintaining tissue homeostasis. Dysregulation of apoptosis is implicated in a variety of diseases, including cancer, neurodegenerative disorders, and autoimmune conditions. This review provides an in-depth insight into the molecular mechanisms and signaling pathways that regulate apoptosis, highlighting both intrinsic and extrinsic pathways. Additionally, the review explains the tumor microenvironment's influence on apoptosis and its implications for cancer therapy resistance. Understanding the complex interplay between apoptotic signaling and cellular responses is crucial for developing targeted therapies that can effectively manage diseases associated with apoptosis dysregulation. The effects of conventional therapeutics and alternative substances with natural sources such as herbal compounds, alongside vitamins, minerals, and trace elements on cellular homeostasis and disease pathogenesis have been thoroughly investigated. Moreover, recent advances in therapeutic strategies aimed at modulating apoptosis are discussed, with a focus on novel interventions such as nutrition bio shield dietary supplement. These emerging approaches offer potential benefits beyond conventional treatments by selectively targeting apoptotic pathways to inhibit cancer progression and metastasis. By integrating insights from recent studies, this review aims to enhance our understanding of apoptosis and guide future research in developing innovative therapeutic approaches.

Objective: Cancer patients frequently report sleep problems. The Pittsburgh Sleep Quality Index (PSQI) is a 19-item instrument for assessing sleep problems. The main objective of this study was to analyze the usefulness of the PSQI in oncological research.

Methods: A sample of 1,733 cancer patients with mixed diagnoses were included. In addition to the PSQI, the following questionnaires were adopted: the Insomnia Severity Index (ISI), the Jenkins Sleep Scale (JSS) and the sleep scale of the EORTC QLQ-SURV100.

Results: The internal consistency of the PSQI was α = 0.79. Of the PSQI subscales, the subjective sleep quality correlated most strongly with the other sleep instruments (r between 0.68 and 0.77). In total, 69.2% of the sample were poor sleepers; the effect size of the difference between the PSQI total scores of the patients and a general population sample was d = 0.83. Female patients experienced more sleep problems than male patients (d = -0.49), and younger patients (<60 years) reported more sleep problems than older patients (≥60 years) (d = 0.21).

Conclusions: The PSQI can be recommended for use in clinical practice since its sub-dimensions provide detailed information on the sleep situation of cancer patients.

Recent research has underscored the pivotal role of chronic inflammation in cancer development. Investigations have elucidated key molecular mechanisms underpinning inflammation-related cancer. Extrinsic pathway, driven by inflammatory conditions and intrinsic pathway, propelled by genetic events, emerged as critical links between inflammation and carcinogenesis. The persistent inflammation exacerbates genomic instability, providing a mechanistic link between inflammation and cancer. Targeting crucial inflammatory pathways such as NFκB, JAK-STAT, MAPK/ERK, PI3K/AKT, Wnt and TGF-β, holds promise for advancing cancer treatment modalities. Hence, understanding the key signalling pathways will highlight the intricate interplay between inflammation and cancer recognizing it as a potential target for interventions.

The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identification and classification of brain tumors and hemorrhages, which can significantly impact patient prognosis and treatment planning. The objectives of the study address the prediction of brain cancer occurrence and the assessment of risk levels associated with both brain cancers due to brain hemorrhage. A diverse dataset of brain MRI and CT scan images. Utilize Unsymmetrical Trimmed Median Filter with Optics Clustering for noise removal while preserving edges and details. The Chan-Vese segmentation process for refined segmentation. Brain cancer detection using Multi-Head Self-Attention Dilated Convolution Neural Network (MH-SA-DCNN) with Efficient Net Model. Brain cancer detection using MH-SA-DCNN with Efficient Net Model. This trains the algorithm to predict cancerous regions in brain images. Further, implement a Graph-Based Deep Neural Network Model (G-DNN) to capture spatial relationships and risk factors from brain images. Cox regression model to estimate cancer risk over time and fine-tune and optimize the model's parameters and features using the Osprey optimization algorithm (OPA).

Despite a biologically established causative role of viral hepatitis (VH), i.e. HBV and HCV infections, on intrahepatic cholangiocarcinoma (ICC), only few large Western cohorts exploring the association between VH and ICC development are available. The prognostic significance of VH in ICC is debated, and no data are available regarding a predictive role for standard first-line CT (CT1), consisting of gemcitabine +/- platinoids. VH-positivity definition is often clinically incomplete and inconsistent among studies. Five different VH conditions, based on laboratory and anamnestic data, were investigated in a multicentric retrospective cohort of advanced ICC cases. Depending on the specific VH condition considered, 139-194 of 472 ICC cases could be categorized according to the presence of the mentioned VH conditions. VH prevalence ranged from 9.3 to 25.3%. No VH condition showed an impact on survival, although a non-significant worse outcome was observed for some HBV-related conditions. HCV-related conditions were associated to lower pre-CT1 biomarkers of inflammation, markedly higher disease control, and numerically longer time-to-progression with CT1. No benefit on time-to-progression was demonstrated for the addition of platinoids to gemcitabine in VH-positive patients (HR 0.77, CI_95% 0.41-1.45), at least in HBV-related cases. These findings are clinically relevant and deserve further investigation.

Background: To investigate the effects of LDHB on lactylation of programmed cell death 1 ligand (PD-L1) and immune evasion of ovarian cancer.

Methods: Ovarian cancer cells were transfected with LDHB siRNA and cultured with primed T cells. Cell proliferation and viability were measured by cell counting kit 8 (CCK-8) and colony formation assay. The production of immune factors was detected by enzyme-linked immunosorbent assay (ELISA). The histone lactylation and activity of PD-L1 promoter were measured by chromatin immunoprecipitation (ChIP)-qPCR assay and luciferase reporter gene assay, respectively.

Results: Knockdown of LDHB notably inhibited the growth, glucose uptake, lactate production, and ATP production of ovarian cancer cells. Knockdown of LDHB enhanced the killing effects of T cells, led to increased production of immune activation factors IL-2, TNF-α, and IFN-γ, as well as elevated the levels of granzyme B and perforin. Mechanical study identified that LDHB regulated the H3K18 lactylation (H3K18la) modification on PD-L1 promoter region to promote its expression. Overexpression of PD-L1 abolished the immune activation effects that induced by siLDHB.

Conclusion: The LDHB modulated lactate production and the histone lactylation on PD-L1 promoter, which ultimately regulated its expression and participated in the immune evasion of ovarian cancer cells.

Skin cancer (SC) is one of the three most common cancers worldwide. Melanoma has the deadliest potential to spread to other parts of the body among all SCs. For SC treatments to be effective, early detection is essential. The high degree of similarity between tumor and non-tumors makes SC diagnosis difficult even for experienced doctors. To address this issue, authors have developed a novel Deep Learning (DL) system capable of automatically classifying skin lesions into seven groups: actinic keratosis (AKIEC), melanoma (MEL), benign keratosis (BKL), melanocytic Nevi (NV), basal cell carcinoma (BCC), dermatofibroma (DF), and vascular (VASC) skin lesions. Authors introduced the Multi-Grained Enhanced Deep Cascaded Forest (Mg-EDCF) as a novel DL model. In this model, first, researchers utilized subsampled multigrained scanning (Mg-sc) to acquire micro features. Second, authors employed two types of Random Forest (RF) to create input features. Finally, the Enhanced Deep Cascaded Forest (EDCF) was utilized for classification. The HAM10000 dataset was used for implementing, training, and evaluating the proposed and Transfer Learning (TL) models such as ResNet, AlexNet, and VGG16. During the validation and training stages, the performance of the four networks was evaluated by comparing their accuracy and loss. The proposed method outperformed the competing models with an average accuracy score of 98.19%. Our proposed methodology was validated against existing state-of-the-art algorithms from recent publications, resulting in consistently greater accuracies than those of the classifiers.

Background & aim: Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples.

Methods & materials: Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy.

Results: The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively.

Conclusion: The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

Background: Evidence with regards to the distinction between primary and metastatic tumors in pancreatic cancer and driving factors for metastases remains limited.

Methods: Single-cell RNA sequencing (scRNA-seq) was conducted on metastatic pancreatic cancer. Bioinformatics analysis on relevant sequencing data was used to construct a risk model to predict patient prognosis. Furthermore, immune infiltration and metabolic differences were assessed. The biological function of key differential genes was evaluated.

Results: Paired primary and metastatic tumor tissues from 3 pancreatic cancer patients were collected and conducted scRNA-seq. Subsequently, the T/NK cell subgroup was the most different cell type between primary tumors and liver metastases and was selected for further analysis. Eventually, 6 specifically expressed genes of T/NK cells (B2M, ZFP36L2, ANXA1, ARL4C, TSPYL2, FYN) were used constructing the prognostic model. The stability of this model was validated by an external cohort. Meanwhile, different immune infiltration abundances occurred between high and low risk groups stratified by the model. The high-risk group had a stronger metabolic capability.

Conclusions: A novel prognostic T/NK-cell signature for pancreatic cancer was constructed based on scRNA-seq data and externally validated. The involved key genes may play a role in multiple metabolic pathways of metastasis and affect the tumor immune microenvironment.