Cancer Investigation最新文献

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.

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.

Since over one-third of DLBCL patients experience relapse or refractory after standard therapy, high-risk patients must be predicted. We developed a prognostic immune-related gene pairs (IRGPs) signature for DLBCL patients using bioinformatics analyses. This signature can predict the prognosis of these patients adequately, either alone or in combination with other clinical parameters. It hopes to improve the stratification and management of these patients for broad clinical applications.

Introduction: The aim of this work was to explore the use of an original once-weekly radiotherapy fractionation in elderly or frail patients with recurrence or metastasis from different solid malignancies.

Material and methods: A retrospective analysis was conducted on 29 patients treated from 2011 to 2019 with a once-weekly radiotherapy schedule. Patients received a median dose of 27.5 Gy, with weekly fractions of 4-5 Gy over 7-10 weeks. The treatment aimed at both palliative and cytoreductive objectives. Primary endpoints were feasibility and compliance, secondary outcome was acute toxicity.

Results: All patients completed the planned radiotherapy without interruptions. Acute toxicity was mild, with only one patient developing grade 3 toxicity (bowel perforation). Three months post-treatment, 74% of patients experienced symptom relief and tumor response. One-year local control rates were 26.7%, and treatment was generally well-tolerated.

Conclusion: This once-weekly hypofractionated radiotherapy regimen demonstrated feasibility, good tolerance, and promising tumor response in frail and elderly patients. Although limited by a small sample size, the approach offers a practical alternative for patients unable to undergo conventional daily radiotherapy. Further studies on larger cohorts are required to validate these findings.

Head and neck squamous cell carcinoma ranks seventh globally in malignancy prevalence, with persistent high mortality rates despite treatment advancements. Glucose, pivotal in cancer metabolism via the Warburg effect, enters cells via glucose transporters, notably GLUT proteins. Glycolysis, aerobic oxidation, and the pentose phosphate pathway in glucose metabolism significantly impact HNSCC progression. HNSCC exhibits elevated expression of glucose metabolism enzymes and GLUT proteins, correlating with prognosis. Heterogeneity in HNSCC yields varied metabolic profiles, influenced by factors like HPV status and disease stage. This review highlights glucose metabolism's role and potential as therapeutic targets and cancer imaging tracers in HNSCC.

Endometrial cancer (EC) is a common gynecological malignancy and its mortality has been increasing in the last twenty years. A growing body of evidence suggests that circulating tumor cells (CTCs) may provide a more complete tumor profile, facilitate the understanding of the molecular mechanism and individual management of EC patients. In this review, we presented the presence and clinical applications of CTCs and disseminated tumor cells (DTCs) in EC, particularly for EC prognosis and management, also highlighted the diagnostic value of tumor cells in urine of EC patients, aim to help researchers better focus on their study in this field.