Demoralization is common among individuals with breast cancer, and both social support and resilience are recognized as protective factors. However, the way these two factors interact at the symptom level remains unclear. This study therefore sought to clarify how resilience modifies the association between social support and demoralization at both symptom and construct levels.

In this cross-sectional analysis, a total of 412 patients with breast cancer from the Be Resilient to Breast Cancer (BRBC) program completed the Perceived Social Support Scale (PSSS), the Cancer-Specific Resilience Scale (RS-SC-10), and the Demoralization Scale-II (DS-II). Moderated network analysis, Johnson-Neyman techniques, and response surface analysis were used to examine the moderating role of resilience from both symptom-level (micro) and scale-level (macro) perspectives.

The mean age of participants was 50.8 years, ranging from 28 to 85 years, and 43.2% of them were diagnosed with stage II cancer. Higher social support and resilience were each negatively associated with demoralization. Resilience significantly moderated the associations between social support and demoralization, including the edge between friends' actual assistance and feeling unsupported (β = -0.002, SE = 0.002, p = 0.024), and between others' emotional care and self-undervaluation (β = 0.013, SE = 0.004, p = 0.004). Demoralization was lowest when patients reported concurrently high social support and resilience, and remained relatively low even when patients reported low social support but high resilience (F = 29.18, P < 0.01).

Resilience appears to be associated with an enhanced protective effect of social support on demoralization and, when high, may be associated with lower demoralization even in the context of low external social support. Interventions that concurrently enhance internal resilience and external social support may be promising strategies to reduce demoralization and improve psychological well-being in patients with breast cancer.

Immune checkpoint inhibitors (ICI) have transformed the treatment of metastatic malignancies, yet only 20-40% of unselected patients benefit. Peripheral blood could offer a minimally invasive and dynamic source for predictive biomarkers of ICI therapy.This study evaluated circulating immune cell frequencies and phenotypes before and during ICI therapy to identify tumor-agnostic blood-based biomarkers. We analyzed routine blood immune cell counts in a retrospective cohort of 202 patients treated with ICIs. Next, we investigated the findings in a prospectively collected cohort of 45 patients using multiparametric flow cytometry for characterization of immune cell subsets. We considered early radiological response, progression-free survival (PFS), and overall survival (OS) as clinical outcomes.Higher pre-treatment monocyte and lower lymphocyte counts were consistently associated with inferior PFS and OS but not with early radiological response in the retrospective cohort. In the prospective cohort, detailed immunophenotyping identified elevated pre-treatment frequencies of intermediate (CD14⁺CD16⁺) monocytes as a marker of poorer PFS and OS. Within lymphocyte subsets, higher T cell frequencies were associated with better OS, and a higher pre-treatment frequency of CD226-expressing CD8⁺ memory T cells with better ICI response. ICI therapy induced increase in TIGIT⁺CD8⁺ and CD4⁺ memory T cell subsets, regardless of treatment response.In conclusion, elevated pre-treatment levels of blood monocytes, together with decreased levels lymphocytes, were associated with poor survival of ICI-treated patients. Although detailed immunophenotyping of pre-treatment blood immune cell populations showed limited predictive utility, specific subpopulations, such as intermediate monocytes and CD226⁺CD8⁺ memory T cells, may harbor potential as prognostic/predictive indicators.

Ropeginterferon alfa-2b is an interferon used in the treatment of myeloproliferative neoplasms, particularly polycythemia vera. Its efficacy in achieving hematologic and molecular responses has been demonstrated in clinical trials, but pooled data on long-term outcomes and sustained response remain limited. This systematic review and meta-analysis aimed to evaluate the hematologic and molecular response over 36 months. PubMed, Scopus, Science Direct, and Google Scholar databases were searched to identify studies reporting hematologic and molecular responses to ropeginterferon alfa-2b. Studies were included if they provided data on complete hematologic response (CHR) and JAK2V617F variant allele frequency (VAF) reduction. Pooled proportions and mean reductions were calculated using random-effects models. The pooled proportion of CHR increased progressively from 0.19 (95% CI: 0.04-0.57) at 3 months to 0.73 (95% CI: 0.17-0.97) at 36 months. Molecular response, measured by VAF reduction, deepened over time from - 7.33 (95% CI: -9.85 to -4.81) at 3 months to -54.90 (95% CI: -65.61 to -43.99) at 36 months. Subgroup analyses revealed significant variability in response rates, particularly in early follow-up periods. Ropeginterferon alfa-2b achieves significant and sustained hematologic and molecular responses over 36 months. This makes it a promising treatment for polycythemia vera. While variability in early responses needs further investigation, the sustained long-term efficacy compared to hydroxyurea supports its use in clinical practice. Future studies should focus on identifying predictors of response and optimizing treatment protocols to maximize patient outcomes.

The classification of mesenchymal neoplasms is undergoing a period of punctuated equilibrium thanks to the increasing availability and routine application of next-generation sequencing. This is especially true for the characterization of undifferentiated/ unclassified mesenchymal tumors, which have historically been grouped morphologically as spindle cell, epithelioid cell, round cell and pleomorphic neoplasms. Herein, we report four undifferentiated epithelioid mesenchymal neoplasms harboring a novel GLI2::CREBBP gene fusion. All patients were adult males who presented with a small superficial mass. Histologically, the tumors were composed of nests or sheets of bland epithelioid cells often embedded within prominent collagenous stroma. One case contained metaplastic bone, and dystrophic calcification in a region of prior ischemia. Immunohistochemistry showed variable keratin expression in three cases, but was otherwise non-contributory. Targeted next generation sequencing revealed a GLI2::CREBBP fusion in all cases. Clinically, three of the tumors followed an indolent clinical course; however, one patient ultimately died as a result of metastatic disease, suggesting GLI2::CREBBP-rearranged nested-hyalinizing epithelioid neoplasms may behave potentially aggressive. Further study is necessary to characterize the biological potential and molecular-pathologic spectrum of this potentially novel entity.

The purpose of this study was to examine whether Ki67-scores have a predictive significance for pathological complete response (pCR) and invasive disease-free survival (IDFS) in HER2-positive breast cancer.

This retrospective, bi-centric cohort study focused on HER2-positive early breast cancer patients undergoing neoadjuvant chemotherapy from 2015 to 2023. Multivariable logistic regression was used to find independent association between various clinical parameters, including Ki67, and pCR. Ki67-values were categorized into three groups (low ≤ 15%, intermediate 15-35%, high > 35%). Kaplan-Meier estimator calculated differences in IDFS.

The study included 244 patients with known Ki67-expression. 147 patients (60.3%) achieved pCR. When categorized, 18 (7.4%) were Ki67 low, 114 (46.7%) Ki67 intermediate and 112 (45.9%) Ki67 high. No correlation between Ki67-score as continuous variable and pCR was observed (p = 0.25). HER2 immunohistochemistry (IHC) score 3 + significantly increased pCR compared to IHC score 2 + (63.2% vs. 45%, p = 0.031). Hormone receptor (HR)-positive tumors had a lower pCR rate (53.1% vs. 74.4%, p = 0.001) compared to HR-negative tumors. 5-year IDFS showed no difference between low Ki67 (88.9%; 95% CI 75.5-100%), intermediate Ki67 (82.0%; 95% CI 72.6-92.7%), and high Ki67 (80.9%, 95% CI 70.1-92.3%) subgroups (p = 0.7).

In HER2-positive breast cancer, the Ki67-score showed no association with either pCR or IDFS, thereby questioning its clinical utility. Conversely the HER2 IHC-score and HR-status were predictive indicators for achieving pCR. Clinical decisions in patients with early HER2-positive breast cancer should not be influenced by Ki67-scores, especially not by using cut-offs.

Peripheral T-cell lymphoma (PTCL) remains a formidable challenge in clinical management. Histone deacetylase inhibitor chidamide has demonstrated its anti-tumor effects in real-world studies in relapsed or refractory PTCL. To evaluate the efficacy of real-world utilization of chidamide combined with chemotherapy for untreated PTCL and explore relative prognostic factors, a cohort of 151 PTCL patients treated with chidamide combined with chemotherapy as front-line treatment in our center were enrolled. The overall response rate (ORR) and complete remission rate (CRR) at the end of treatment were 81.5% and 67.5%. The 7-year overall survival (OS) rate and progression-free survival (PFS) rate were 67.6% and 49.7%, with a median follow-up period of 21 months, showing its satisfactory efficacy and survival advantage. Most of adverse events were transient and reversible. Furthermore, several baseline characteristics of patients were relevant to prognosis. The study identified gene mutations in TET2 (30.9%), STAT (22.7%), RHOA (17.5%), TP53 (14.4%), DNMT3A (12.4%), with TP53 and DNMT3A mutations correlating with worse clinical outcomes. The identification of gene mutations contributed to personalizing treatment strategies and predicting patient outcomes. This study raised the preliminary hypothesis that chidamide-containing front-line therapy might be promising for PTCL patients, which warranted further investigation.

IntroductionLow- and middle-income countries (LMICs) like Nigeria face rising cancer incidence and mortality, with late-stage presentation and limited resources. Only eight government-funded radiotherapy centres serve a population of 223.8 million-far below the estimated 280 radiotherapy machines required. To increase patient throughput we evaluated integration of AI auto-contouring tools to expedite treatment planning, specifically target and organ-at-risk delineation.Materials and MethodsWe performed an observational, survey-based study of radiation oncology staff at our Cancer Centre. Participants were consultant and resident oncologists and medical physicists. The survey compared time spent using AI auto-contouring versus manual contouring and collected perceptions of impact, benefits, and limitations.ResultsThirty-one staff responded: 20 (64.5%) oncologists and 11 (35.5%) medical physicists. Experience with AI varied (33% ≤ 6 months; 13% ≈2 years). Respondents reported increased confidence in planning: 11 (35%) moderate, 12 (39%) moderate-high, and 8 (26%) high. Common limitations were licence availability (20, 64.5%) and technical expertise (19, 61.3%). Most respondents (20, 65%) would recommend the tool. The principal benefit was improved workflow efficiency (25, 81%). AI-assisted planning significantly reduced planning time for most tumour sites; sites with complex anatomy showed no time benefit, reflecting the need for intensive manual correction.ConclusionDeployment of AI auto-contouring at a Nigerian cancer centre reduced planning time for most sites and improved clinician confidence, but complex anatomical regions still require detailed manual oversight and additional AI training. AI tools can increase throughput in LMIC radiotherapy services, though licensing, infrastructure, and training barriers exist and must be addressed to ensure safe implementation. Future work should include multi-centre validation, formal inter-rater reliability assessment, and prospective patient-level outcome evaluation and cost-effectiveness analyses.

ObjectiveOccupational exposure to carcinogens significantly contributes to the global burden of tracheal, bronchial, and lung (TBL) cancers. This study aims to quantify the global, regional, and national burden of TBL cancers attributable to occupational carcinogens using Global Burden of Disease (GBD) 2021 data and project trends to 2050. Additionally, we employ Mendelian Randomization (MR) to explore potential causal relationships between modifiable risk factors and TBL cancers.MethodsWe extracted mortality and Disability-Adjusted Life Year (DALY) data for TBL cancers caused by occupational carcinogens from the GBD 2021 database. Exponential smoothing and autoregressive integrated moving average (ARIMA) models projected the burden to 2050. Two-sample MR analysis utilized genome-wide association study (GWAS) data, primarily from individuals of European ancestry, to investigate causal links.ResultsIn 2021, occupational carcinogens caused 285,628 deaths and 6.12 million DALYs globally. While age-standardized mortality and DALY rates declined in some high-income countries, low- and middle-income countries (LMICs) showed rising trends. Projections indicate a potential shift, with some regions plateauing while others face increasing burdens due to persistent exposure. MR analysis confirmed significant causal relationships, identifying higher BMI, smoking, visceral adiposity, and waist circumference as risk factors, while coffee consumption, dried fruit intake, physical activity, and education were protective.ConclusionDespite progress, the burden of occupational TBL cancers remains substantial, particularly in LMICs. The discordance between declining rates in high-income nations and rising burdens elsewhere highlights the need for targeted interventions and stricter regulations. Integrating genetic evidence supports precision prevention strategies focusing on both occupational safety and modifiable lifestyle factors.

Characterisation of CT detected ovarian masses is challenging with overlapping imaging features, unreliable biomarker or clinical presentation. We proposed a two-staged CT-based radiomics model to identify early-stage ovarian carcinoma (ES-OC) and sub-classify different types of benign ovarian masses (BOM).

Patients with histologically confirmed BOM or ES-OC (FIGO I-II) were retrospectively recruited from 5 centres. Radiomics features were derived from CT images using PyRadiomics (v3.0.1), which intrinsically resampled volumes to isotropic 1 mm³ voxels. To reduce feature redundancy, features with high correlation (Spearman's ρ ≥ 0.85) were excluded. Two-staged feature selection was applied. First, elastic-net regression with repeated 5-fold stratified cross-validation (100 iterations) was performed to identify highly repeatable features, followed by Mann-Whitney U testing for statistical significance. Second, Boruta algorithm with Random Forest (RF) estimator was employed over 500 iterations to robustly select features by comparing their importance to randomized shadow features. Several machine learning (ML) classifiers were evaluated using stratified 10‑fold GridSearch cross-validation with area under the curve (AUC) as tuning metric. The optimal model from each stage with highest cross-validated AUC was then evaluated on the respective test set. The AUC, calibration plot, and decision curve analysis (DCA) were employed to assess the performance and clinical utility of models.

The study enrolled 483 patients with 529 lesions (ES-OC: 192 patients, 192 lesions; BOM: 291 patients, 337 lesions). In the first-stage, logistic regression (LR) algorithm was selected with high sensitivity (0.870), moderate specificity (0.719) and high AUC (0.859) in the test set. In the second-stage, support vector machines (SVM) had high diagnostic accuracy with sensitivity 0.750, specificity 0.839 and AUC 0.918. DCA identified the highest benefit at 0.20 risk threshold probability in determining ES-OC.

The two-staged CT-based radiomics model incorporating LR and SVM algorithms had high diagnostic efficiency in characterising ES-OC and BOM, potentially in triaging disease and personalising care.

Lung cancer is one of the most common malignancies and the leading cause of cancer-related mortality worldwide, posing a major public health challenge. Flavonoids, a large and diverse group of plant metabolites, exhibit various anticancer properties, making them promising candidates for therapeutic applications. This study evaluated the anticancer efficacy of methoxy flavonoids and elucidated their underlying mechanisms of action in A549 lung cancer cells.

A549 cells were treated with various flavonoids (AKC1-AKC5), and their effects were analyzed using an MTT assay, DAPI staining, mitochondrial membrane potential (MMP), reactive oxygen species (ROS) production, colony formation, and wound scratch tests. Molecular docking was also performed to confirm the binding of AKC1 and AKC3 to EGFR, BCL-2, and CDK-2 proteins.

AKC1 and AKC3 prevented the growth of A549 lung cancer cells with IC50 of 64.57 and 19.80 μM among 5 methoxy flavonoids. AKC1 and AKC3 triggered notable alterations in the shape and reduced the colony-forming potential of A549 cells. The DAPI staining experiment demonstrated that AKC1 and AKC3 impede the growth of cancer cells through activation of apoptotic cell death. Moreover, the anticancer properties of AKC1 and AKC3 were attributed to significant inhibition of MMP and a notable ROS enhancement in a dose-related pattern. The wound scratch assay demonstrated that AKC1 and AKC3 suppressed A549 lung cancer cell migration, suggesting their anti-metastatic properties. Molecular docking studies confirmed that AKC-1 and AKC-3 bind strongly to EGFR, BCL-2, and CDK2, suggesting a multi-target mechanism that underlies their anti-proliferative and pro-apoptotic effects in A549 cells.

AKC1 and AKC3 exhibited significant anticancer activity against A549 cells and may serve as promising therapeutic drugs for lung cancer treatment.