American journal of cancer research最新文献

The tumor immune microenvironment (TIME) plays a critical role in cancer development and response to immunotherapy. Immune checkpoint inhibitors aim to reverse the immunosuppressive effects of the TIME, but their success has been limited. Immunotherapy directed at PD-1/PD-L1 has been widely employed, yielding positive results. Unfortunately, the gradual emergence of resistance to PD-1/PD-L1 inhibition has diminished the effectiveness of this immunotherapy in cancer patients, emphasizing the need for new compounds that will be more effective in managing immunotherapy. This study investigated the effect of the natural compound cardamonin on PD-L1 expression and its ability to modulate the TIME, which could overcome immunotherapy resistance in triple-negative breast cancer (TNBC). This investigation used two genetically distinct triple-negative breast cancer cell lines, MDA-MB-231 (MDA-231) and MDA-MB-468 (MDA-468). The results show that TNBC cell treatment with cardamonin inhibited PD-L1 expression and reduced JAK1 and STAT3 levels in MDA-231 cells, while it increased JAK1 expression in MDA-468 cells. Also, cardamonin increased the expression of Nrf2 in both cell lines. In addition, cardamonin decreased MUC1, NF-κB1, and NF-κB2 expression in MDA-MB-231 cells and selectively reduced NF-κB1 expression in MDA-468 cells. Furthermore, cardamonin very potently reduced the inflammatory cytokine CCL2 levels. The decrease in CCL2 release reduces the chemoattraction of macrophages in the tumor microenvironment, which may increase the effectiveness of PD-1/PD-L1 inhibition and allow T-cell infiltration. These findings suggest that the cardamonin modulation of TIME holds promise in reversing resistance of PD-1/PD-L1 inhibition when it is used along with immunotherapy in TNBC treatment.

Cancer cell overexpresses numerus proteins, however, how these up-regulated proteins, especially those enzymatically opposite kinases and phosphatases, act together to promote oncogenesis is unknown. Here, we reported that protein tyrosine phosphatase H1 (PTPH1) is a scaffold protein for receptor tyrosine kinase (HER2) to potentiate breast tumorigenesis. PTPH1 utilizes its PDZ domain to bind HER2, p38γ, PBK, and YAP1 and to increase HER2 nuclear translocation, stemness, and oncogenesis. PTPH1 de-phosphorylates HER2 and reciprocally increases HER2 protein expression dependent on cellular content. PTPH1 itself can be phosphorylated at S459 by redundant kinases p38γ and/or PBK, thereby distinctively regulating expression and/or turnover of scaffold proteins. Moreover, PTPH1 and HER2 cooperate to increase PBK and Yap1 transcription thus acting as an additional mechanism to activate the scaffold. PTPH1 protein levels are higher in HER2⁺ breast cancer in which their phosphorylated forms are inversely correlated, indicating an integrated oncogenic activity through coordinated PTPH1 phosphorylation and HER2 de-phosphorylation. Combinational, but not individual, application of scaffold-kinases' inhibitors suppresses xenograft growth in mice. Thus, a PDZ-coupled and phosphorylation-driven scaffold can integrate proliferative signaling of enzymatically distinct proteins as a super-oncogene and as a target for combination therapy.

This study aimed to explore the risk factors for mediastinal lymph node metastases (MLNM) in patients with early-stage non-small-cell lung cancer (NSCLC) and to establish a predictive model. A retrospective analysis was conducted on the clinical data from NSCLC patients treated at the Second Affiliated Hospital of Guangzhou Medical University and the First Affiliated Dongguan Hospital of Guangdong Medical University between March 2021 and March 2023. Baseline clinical data, laboratory parameters, and pathological features were collected and analyzed. Univariate and multivariate logistic regression identified several independent risk factors for MLNM, including Cyfra21-1, D-dimer (D-D), tumor size, percentage of tumor solid, and lesion location. These risk factors were incorporated into a Nomogram model to visually assess the likelihood of MLNM. The model demonstrated excellent diagnostic accuracy with an area under the curve (AUC) of 0.904, a specificity of 73.85%, and a sensitivity of 93.68%. Cyfra21-1 and D-D were particularly significant predictors of MLNM. This Nomogram model provides an effective and practical tool for assessing MLNM risk in early-stage NSCLC, aiding clinical decision-making and optimizing treatment strategies.

Breast cancer is one of the malignant tumors that seriously threaten women's health, and early diagnosis and detection of breast cancer are crucial for effective treatment. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important diagnostic tool that allows for the dynamic observation of blood flow characteristics of breast tumors, including small lesions within the affected tissue. Currently, it is widely used in clinical practice and has been shown promising prospects. This study included a total of 1,987 patients who underwent breast surgery at Huangpu Branch, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine from January 1, 2019 to December 31, 2019. Comprehensive patient information was collected, including ultrasound, mammography findings, physical examination details, age, family history, and pathological diagnoses. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to assign values to the x variables, facilitating the construction and validation of the LASSO model group. Receiver operating characteristic curves were generated using support vector machines to determine the area under the curve (AUC), as well as to assess sensitivity and specificity. There were no statistically significant differences (P>0.05) in average age, body mass index, tumor location, or tumor benignity/malignancy between the training and test sets. The AUC, sensitivity, and specificity of mammography for predicting the benignity or malignancy of breast tumors were 0.83, 86.96%, and 76%, respectively. In comparison, the AUC, sensitivity, and specificity of DCE-MRI for the same predictions were 0.91, 91.3%, and 88%, respectively. The predictive performance of DCE-MRI was significantly higher than that of mammography (P<0.05). In conclusion, both mammography and DCE-MRI demonstrated high AUC, sensitivity, and specificity in predicting the benignity or malignancy of breast tumors. However, DCE-MRI showed superior predictive performance, making it a valuable tool for the early detection of clinical breast cancer with potential for broader clinical application.

The use of routine adjuvant radiotherapy (RT) after breast-conserving surgery (BCS) is controversial in elderly patients with early-stage breast cancer (EBC). This study aimed to evaluate the efficacy of adjuvant RT for elderly EBC patients using deep learning (DL) to personalize treatment plans. Five distinct DL models were developed to generate personalized treatment recommendations. Patients whose actual treatments aligned with the DL model suggestions were classified into the Consistent group, while those with divergent treatments were placed in the Inconsistent group. The efficacy of these models was assessed by comparing outcomes between the two groups. Multivariate logistic regression and Poisson regression analyses were used to visualize and quantify the influence of various features on adjuvant RT selection. In a cohort of 8,047 elderly EBC patients, treatment following the Deep Survival Regression with Mixture Effects (DSME) model's recommendations significantly improved survival, with inverse probability of treatment weighting (IPTW)-adjusted benefits, including a hazard ratio of 0.70 (95% CI, 0.58-0.86), a risk difference of 4.63% (95% CI, 1.59-7.66), and an extended mean survival time of 8.96 months (95% CI, 6.85-10.97), outperforming other models and the National Comprehensive Cancer Network (NCCN) guidelines. The DSME model identified elderly patients with larger tumors and more advanced disease stages as ideal candidates for adjuvant RT, though no benefit was seen in patients not recommended for it. This study introduces a novel DL-guided approach for selecting adjuvant RT in elderly EBC patients, enhancing treatment precision and potentially improving survival outcomes while minimizing unnecessary interventions.

This study aims to construct and optimize risk prediction models for lymph node metastasis (LNM) in endometrial carcinoma (EC) patients, thus improving the identification of patients at high risk of LNM and further providing accurate support for clinical decision-making. This retrospective analysis included 541 cases of EC treated at The First Affiliated Hospital, Jinan University between January 2017 and January 2022. Various clinical and pathological variables were incorporated, including age, body mass index (BMI), pathological grading, myometrial invasion, lymphovascular space invasion (LVSI), estrogen receptor (ER) and progesterone receptor (PR) levels, and tumor size. Multivariate Logistic regression analysis was used to identify independent risk factors for LNM. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), RandomForest, and Support Vector Machine (SVM), all machine-learning algorithms, were adopted to select features and build models. The XGBoost model gave the best performance among all models, with areas under the curve (AUCs) of 0.876 and 0.832 for training and validation sets, respectively, suggesting its high discriminatory ability and prediction accuracy. Moreover, the calibration curve analysis further verified the consistency of the model-predicted values with the actual results, indicating the model's good applicability at various risk levels. According to the decision curve analysis, the XGBoost model showed high net benefits within most risk-threshold ranges, indicating its substantial practical value in clinical applications. Conclusively, this study successfully builds machine-learning models based on multiple clinical and pathological features, which can effectively predict the LNM risk in EC patients. The model is expected to provide important references for clinicians in surgical decision-making and the formulation of individualized treatment plans, thereby enhancing patient outcomes.

This study investigated the predictive value of combining peripheral blood indicators with procalcitonin clearance rate (PCTc) to assess mortality risk in cancer patients with sepsis, aiming to develop a more sensitive and specific clinical tool. A retrospective analysis was conducted on 393 cancer patients with sepsis admitted to South China Hospital of Shenzhen University from January 2019 to January 2024. Collected data included clinical demographics, laboratory indicators such as white blood cell count, neutrophil count (NEUT), platelet count (PLT), lymphocyte count (LYC), C-reactive protein, procalcitonin (PCT), alanine aminotransferase, and the ratio of arterial oxygen partial pressure to inspired oxygen fraction, as well as functional scores like Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment. Multivariate logistic regression and receiver operating characteristic curves assessed the predictive ability of these factors for 28-day survival. Results showed significantly higher NEUT (P<0.001) and lower PLT and LYC (P<0.001) in the death group, while APACHE II score (area under the curve (AUC) = 0.776) and PCT 24h (AUC = 0.723) demonstrated strong predictive value for mortality risk. The joint projection model's AUC reached 0.966, significantly outperforming individual indicators, indicating that combining multiple indicators offers a more accurate prediction of survival versus mortality risk. Additionally, 24h LCR and 24h PCTc were notably lower in the death group compared to the survival group, reinforcing the advantage of combined indicators for prognosis. Overall, using both peripheral blood indicators and PCTc significantly improves the accuracy of mortality risk assessment in cancer patients with sepsis, enhancing prognostic evaluation and supporting optimized clinical decision-making.

We investigated if selected polymorphisms in DNA repair genes modify the association between exposure to particulate matter ≤ 10 micron in diameter (PM₁₀) and breast cancer (BCa) risk. We included 150,929 postmenopausal women (5,969 with BCa) from UK Biobank, a population-based prospective cohort. Cancer diagnoses were ascertained through the linkage to the UK National Health Service Central Registers. Information on BCa risk factors was collected at baseline. Blood samples were collected from participants at enrollment and genotyped using the Applied Biosystems UK BiLEVE Axiom Array or the Applied Biosystems UK Biobank Axiom Array. Cox proportional hazards regression was used to examine interactions of exposure (2007 PM₁₀ and cumulative average PM₁₀) with 14 SNPs, adjusting for BCa risk factors. The positive associations of 2007 PM₁₀ and cumulative average PM₁₀ with BCa risk were stronger in women with one or two copies of XRCC2 rs3218536 C allele vs. none (2007 PM₁₀ Hazard Ratio [HR] per 10 µg/m³ = 1.54, 95% Confidence Interval [CI] 1.22, 1.95 or HR = 1.14, 95% CI 1.03, 1.30 vs. HR = 0.52, 95% CI 0.16, 1.75, p-interaction = 0.02; cumulative average PM₁₀ HR per 10 µg/m³ = 2.80, 95% CI 1.99, 3.96 or HR = 1.89, 95% CI 1.64, 2.18 vs. HR = 0.45, 95% CI 0.08, 2.37, p-interaction = 0.05). We observed no interactions of PM₁₀ with other SNPs. Our results suggest stronger associations of 2007 PM₁₀ and cumulative average PM₁₀ with postmenopausal BCa risk in carriers of XRCC2 rs3218536 C allele.

Esophageal squamous cell carcinoma (ESCC), the most predominant subtype of esophageal cancer, is notorious for its high lymph node metastatic potential and poor prognosis. Growing evidence has demonstrated crucial function of circRNAs in human malignancies. However, the knowledge of circRNAs in lymph node metastasis of ESCC is still inadequate. In this study, a series of bioinformatic analyses and experimental validation were performed. By performing differential expression analysis and selection for GEO dataset GSE150476, a total of 8 circRNAs associated with lymph node metastasis of ESCC were identified. Expression analysis confirmed their low expression in ESCC tissues (relative to normal tissues) or metastatic sites (relative to primary sites). By combination of binding miRNAs from CSCD and starBase databases, six potential miRNAs (miR-532-5p, miR-2681-5p, miR-670-5p, miR-1252-5p, miR-382-3p and miR-542-3p) were predicted and a circRNA-miRNA regulatory network was constructed. Next, 695 target genes were predicted to bind to the 6 miRNAs. After conducting protein-protein interaction (PPI) network analysis, hub gene identification and expression analysis, a hub gene PIK3R1 was identified as the most potential downstream target gene of hsa_circ_0087104/miR-542-3p in ESCC. Hsa_circ_0087104 and PIK3R1 were decreased while miR-542-3p was increased in ESCC cells compared with normal esophageal epithelial cell line. Luciferase reporter and MS2-RIP assays confirmed the direct bind of miR-542-3p to hsa_circ_0087104 or PIK3R1. Hsa_circ_0087104 increased PIK3R1 expression but ectopic expression of miR-542-3p reversed hsa_circ_0087104-mediated PIK3R1 overexpression in ESCC. Overexpression of hsa_circ_0087104 suppressed in vitro migration and invasion of ESCC cells and this suppressive effect could be weakened by upregulation of miR-542-3p. Collectively, the current findings elucidated a potential hsa_circ_0087104/miR-542-3p/PIK3R1 axis that might be involved in suppression of lymph node metastasis of ESCC.

Background: Ultra-low rectal endoscopic submucosal dissection (ESD) presents technical challenges due to anatomical features. The objective of this research was to determine the risk factors linked to unsuccessful curative resections and to create a nomogram predictive model to assess the likelihood of encountering technical challenges.

Methods: Patients with ultra-low rectal tumors received ESD form June 2017 to December 2022 were retrospectively enrolled. An ESD procedure exceeding 30 min was deemed difficult. A logistic regression analysis was performed to pinpoint important factors and predictors. The effectiveness of the nomogram, which incorporated the identified predictors, was evaluated by employing receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Results: A total of 300 patients with ultra-low rectal tumors were enrolled, with a curative resection rate of 82.0%. Multivariate logistic regression revealed that poor lifting sign (OR = 3.282, P = 0.026), non-granular type laterally spreading tumors (LST-NG, OR = 2.230, P = 0.042) and procedure time ≥ 60 min (OR = 6.976, P = 0.010) contributed to non-curative resection. Predictors for ESD difficulty included tumor diameter ≥ 30 mm (compared with < 30 mm, 30-50 mm, OR = 2.450, P = 0.044; ≥ 50 mm, OR = 5.047, P = 0.009), ≥ 1/2 circumference involvement (OR = 3.183, P = 0.038); dentate line invasion (OR = 3.881, P = 0.026) and less colorectal ESD experience (OR = 3.415, P = 0.032). The nomogram performed well in both train and validation sets (area under the curve (AUC) = 0.873 and 0.810, respectively). Calibration plots exhibited satisfactory agreement between predicted and observed outcomes, and DCA showed superior clinical benefit of the model than individual predictors.

Conclusions: Poor lifting sign, LST-NG and procedure time ≥ 60 min were associated with non-curative resection for ultra-low rectal ESD. By including factors such as tumor size, location, and the operator's experience with ESD, the nomogram can predict the complexity of the procedure before surgery.