Journal of Cancer最新文献

Introduction: Bladder cancer is the second most common urological malignancy worldwide, with significant morbidity and mortality. This study investigates the association between chronic indwelling catheter (CIDC) use and bladder cancer risk, particularly in relation to comorbidities and complications. Methods: Taiwan's National Health Insurance Research Database between 2007 and 2018 was used in this study. Patients with CIDC were identified based on records of catheterization on more than six occasions and matched with two patients without CIDC by age, gender, and index date. The outcome, bladder cancer, was identified using ICD-O-3: C67. The incidence rate of bladder cancer was calculated as the number of bladder cancer cases divided by the total follow-up years during the study period. Cox hazards model was also used to adjust with potential confounding variables. Results: A total of 72,971 CIDC patients and 145,942 matched controls were analyzed. The incidence rate of bladder cancer in the CIDC group was 213.29 per 100,000 person-years, significantly higher than 40.4 per 100,000 person-years in the control group with incidence rate ratio: 5.23 (95% CI: 4.60-5.94, p<.0001). After adjusting with confoundings, patients with CIDC show a 5.16-fold higher risk of bladder cancer compared to those without (95% CI, 4.35-6.13, p<.0001). Subgroup analysis revealed a stronger association in younger patients and females. CIDC-related complications, such as urinary tract stones and benign prostatic hyperplasia (BPH), further increased bladder cancer risk. Conclusion: Our findings suggest a strong association between CIDC use and increased risk of bladder cancer, especially among younger patients and those with urological complications such as BPH and urinary tract stones. Additionally, comorbidities including chronic kidney disease, hypertension, and chronic obstructive pulmonary disease may contribute to this elevated risk. Therefore, an integrated healthcare strategy, including monitoring of comorbidities and complications, early cancer detection, and regular risk assessment, is critical for physicians to effectively manage bladder cancer risk in this population.

Background: To evaluate the impact of Huisheng Oral Solution (HSOS) in conjunction with immune checkpoint inhibitors (ICIs) and chemotherapy on patients with stage III-IV non-small cell lung cancer (NSCLC). Methods: This retrospective study included patients with stage III-IV NSCLC who were treated at Sichuan Provincial People's Hospital from May 2018 to June 2021. Patients were categorized into two groups: the ICIs & Chemo Group and the ICIs & Chemo & HSOS Group, based on the therapies administered. The disease control rate (DCR), objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and immune-related adverse events (irAEs) were assessed. Results: A total of 185 patients were included, with 109 patients in ICIs & Chemo & HSOS Group. The ICIs & Chemo & HSOS Group exhibited significantly enhanced DCR (90.83% vs. 71.05%, p=0.001) compared to the ICIs & Chemo Group. The ORR was not statistically significant between the two groups (31.19% vs. 27.63%, p=0.628). Patients in the ICIs & Chemo & HSOS Group had significantly longer PFS (HR=0.47, 95% CI: 0.29-0.75, p<0.001) and OS (HR=0.58, 95% CI: 0.33-1.00, p=0.037) than the ICIs & Chemo Group. In terms of irAEs, nephrotoxicity (5.77% vs. 15.25%, p=0.044), checkpoint inhibitor-related pneumonitis (CIP) (2.75% vs. 11.84%, p=0.014), and cardiotoxicity (0% vs. 13.04%, p=0.026) were significantly lower in the ICIs & Chemo & HSOS Group. Conclusion: The addition of HSOS to ICIs and chemotherapy may enhance DCR, PFS, and OS, while concurrently reducing irAEs in patients with stage III-IV NSCLC. These findings suggest that HSOS may serve as a promising adjunct to ICI-based therapies. Further prospective studies are warranted to validate these results.

Objective: Osteosarcoma is a highly aggressive primary bone tumor that predominantly affects pediatric and adolescent populations. This study aims to explore the therapeutic potential and underlying mechanisms of melittin in treating osteosarcoma both in vitro and in vivo. Materials and methods: Two osteosarcoma cell lines, namely 143B and U-2 OS, were utilized to assess the impact of melittin on cellular proliferation, apoptosis, and cell cycle progression. The effect of melittin on cell viability was evaluated using the CCK-8 assay. Flow cytometry was employed to assess apoptosis and cell cycle progression, while Western blotting analyzed the expression of key proteins, including Cdc-2, c-Myc, and ꞵ-catenin. Specifically, we employed small interfering RNA (siRNA) to selectively knock down the expression of β-catenin in two osteosarcoma cell lines. In vivo studies, tumor growth in nude mice was evaluated through measurements of tumor weight, volume. Additionally, immunohistochemical analysis was performed to assess Ki-67 and active ꞵ-catenin expression in tumor tissues. Results: Melittin induced apoptosis in 143B and U-2 OS osteosarcoma cells in a concentration-dependent manner and caused S-phase cell cycle arrest as the drug concentration increased. Mechanistic investigations revealed that melittin's efficacy was associated with the inactivation of the Wnt/ꞵ-catenin signaling pathway. Specifically, melittin treatment reduced the expression of phosphorylated glycogen synthase kinase-3 beta (p-GSK-3ꞵ) and active ꞵ-catenin. Notably, silencing β-catenin in 143B and U-2 OS osteosarcoma cells significantly enhanced the anti-proliferative and pro-apoptotic effects of melittin. This was evidenced by reduced cell viability and increased apoptosis, thereby further confirming that the anti-tumor efficacy of melittin is dependent on the inhibition of the Wnt/β-catenin signaling pathway. In vivo studies confirmed that melittin significantly inhibited the growth of subcutaneously implanted tumors in nude mice. This inhibition was further supported by Ki-67 analyse and H&E staining, while immunohistochemical analysis revealed an obvious reduction in active ꞵ-catenin expression. Conclusions: Our results offer deeper mechanistic insights into the inhibitory impact of melittin on osteosarcoma progression, at least in part by suppressing the Wnt/β-catenin signaling pathway. We expand upon previous research by providing more comprehensive and robust evidences that underscore the potential of melittin as a viable and safe therapeutic agent for osteosarcoma.

Spermidine metabolism influences tumor progression and anti-tumor immunity, thereby affecting treatment sensitivity. However, the precise role and therapeutic potential of spermidine in breast cancer remain unclear. Integrated multi-omics analyses (bulk and single-cell RNA sequencing) revealed a significant positive correlation between intratumoral spermidine abundance and immunophenotypic markers of CD8⁺ T cell infiltration and activation (GZMB⁺CD8⁺ T cells). Immunohistochemical and multiplexed immunohistochemistry validation (IHC/mIHC) demonstrated that breast cancer specimens with elevated spermidine production exhibited increased numbers of activated CD8⁺ T cells. Exogenous supplementation with spermidine promoted CD8⁺ T cell activation directly. Furthermore, supplementing spermidine in vivo promoted anti-tumor immune responses and enhanced sensitivity to anti-PD-1 immunotherapy combined with chemotherapy. Our findings indicate that boosting spermidine metabolism is a promising strategy to reinvigorate CD8⁺ T cell function and improve the efficacy of checkpoint blockade immunotherapy.

Purpose: To investigate the use of heat shock protein 90alpha (HSP90α) as a marker for prognostic evaluation and efficacy monitoring in patients receiving PD-1 inhibitors treatment for advanced gastric cancer (AGC). Methods: We investigated the value of HSP90α in AGC patients treated with PD-1 inhibitors from a clinical perspective using human plasma samples. Results: In summary, plasma HSP90α was significantly associated with neutrophil-to-lymphocyte (NLR) in AGC patients at baseline. Regarding short-term efficacy, HSP90α levels decreased considerably after PD-1 inhibitor treatment in the partial response (PR) group (P=0.016). Furthermore, there was no significant difference between HSP90α levels in stable disease (SD) group before and after immunotherapy (P=0.659). However, HSP90α levels were considerably greater in AGC patients at disease progression and eventual PD-1 inhibitor therapy failure compared to baseline (P=0.041, P=0.005). Notably, plasma HSP90α, treatment lines, metastatic sites, and NLR level were independent predictive variables for overall survival (OS) in AGC patients receiving PD-1 inhibitors treatment before and after propensity score matching. Additionally, we constructed the nomogram model depending on the above independent prognostic variables, which can well differentiate the clinical prognosis of patients (P<0.001). And ROC curves, calibration curves, and decision curve analysis curves revealed promising discrimination and accuracy of the nomogram. Finally, plasma HSP90α showed specific prognostic value in different subgroups of clinical characteristics. Conclusion: Plasma HSP90α can be used as a marker for efficacy monitoring and prognostic assessment in AGC patients receiving PD-1 inhibitors treatment. We combined plasma HSP90α, NLR, and clinical characteristics to construct a nomogram for predicting the prognosis of gastric cancer immunotherapy, providing a powerful tool for clinical decision-making.

Cancer prognosis relies not only on genetic and molecular biomarkers but also on the spatial organization of tumor and immune cells within the tumor microenvironment. Recent advances in spatial biology, particularly hyperplex immunofluorescence (IMF) imaging, have enabled high-dimensional, quantitative assessment of cell-cell interactions at the protein level. Nearest neighbor analysis (NNA) and proximity analysis have emerged as crucial computational methods for quantifying spatial distributions of tumor, stromal, and immune cells in hyperplex IMF datasets, providing insights into tumor heterogeneity, immune infiltration, and treatment response. This review explores the current state of nearest neighbor and proximity analysis in cancer research, focusing on their applications in prognosis using single-cell spatial proteomics data generated by hyperplex IMF imaging. We summarize key computational approaches, including nearest neighbor distance metrics, Ripley's K-function, Voronoi tessellation, and graph-based models, that characterize spatial architecture within the tumor microenvironment. We highlight recent applications of hyperplex IMF in cancers showcasing how spatial proteomic signatures improve prognostic models. Furthermore, we discuss the integration of machine learning and AI-driven methods to leverage these spatial features for predictive modeling. Despite significant progress, challenges remain, including standardization of methodologies, variability in imaging technologies, and the need for large-scale, high-quality datasets. Addressing these challenges could lead to more accurate risk stratification and personalized treatment strategies. By providing a comprehensive overview of nearest neighbor and proximity analysis in the context of hyperplex IMF-based spatial proteomics, this review aims to bridge the gap between computational methodologies and clinical applications, offering new perspectives on how spatial organization at the protein level influences cancer prognosis.

The acquisition of resistance to anoikis is a critical driver of metastasis in various tumor types. However, the combined role of anoikis apoptosis in the progression and prognosis of hepatocellular carcinoma (HCC) remains largely unexplored. This study integrates known anoikis genes with single-cell datasets to identify differentially expressed Anoikis (DE-Anoikis) through unsupervised clustering, enabling the classification of samples from The Cancer Genome Atlas (TCGA). A prognostic risk model was constructed using univariate Cox proportional hazards regression and validated with external datasets from the International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO). The results revealed significant prognostic differences among DE-Anoikis-based HCC molecular subtypes, with functional enrichment analyses highlighting metabolic reprogramming differences. Furthermore, the anoikis-related prognostic model demonstrated robust predictive accuracy across multiple validation datasets. Two potential therapeutic drugs exhibited sensitivity in low-risk patients, offering novel insights into HCC treatment. Overall, this study identifies a unique subgroup of apoptosis-associated HCC and a prognostic model, providing further biological insights into the molecular mechanisms and therapeutic strategies for HCC.

Background: Triple-negative breast cancer (TNBC) exhibits a higher propensity for recurrence, distant metastasis, and mortality than the other subtypes of breast cancer. TNBC is primarily attributed to the lack of expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Methods: Single-cell sequencing results of CD8⁺ T cells in TNBC patients were screened for differentially expressed and immune-related genes. The selected genes were then analyzed with immunohistochemistry for their prognostic effects. Additionally, a regression model was constructed to ascertain the gene expression score and classify patients into high- and low-risk groups. We further analyzed the impact of gene expression on prognosis based on risk grouping and evaluated its potential as a prognostic predictor for TNBC patients. This analysis was validated using PCR and the prognostic data from patient samples. We also investigated the effect of risk grouping on immunotherapy in TNBC patients and evaluated its potential to predict the efficacy of immunotherapy in TNBC patients. Results: Single-cell sequencing of CD8⁺ T cells from TNBC patients identified 191 differentially expressed genes. Among them, XCL1, RASGRP1, CTSD, and AIP were reported to be independent prognostic factors for TNBC. The results were verified through immunohistochemistry. Additionally, a regression analysis model was constructed using these four genes to classify patients into risk groups. The high-risk group correlated with a poor prognosis in patients and could serve as an independent prognostic factor for TNBC. The results were further validated through PCR. Notably, patients in the low-risk group displayed a better response to immunotherapy. Conclusion: Based on the single-cell sequencing results of CD8⁺ T cells from TNBC patients, a prediction model was established, which facilitated prognosis prediction in TNBC patients and evaluated the patients' response to immunotherapy. In summation, this model could potentially assist in improving the efficacy of TNBC immunotherapy.

Shugoshin 1 (SGO1) is primarily known for its critical functions in chromosome segregation during cell division, protecting cohesin complexes and ensuring accurate mitotic processes. Previous studies have reported SGO1's regulatory roles in isolated cancer types, but its pan-cancer significance and underlying mechanisms remain undefined. This study systematically investigates SGO1 in 33 cancer types, integrating multi-omics analyses and functional validation to reveal its role as a pan-cancer biomarker and therapeutic target. Using TCGA, GEPIA2, and HPA databases, we found SGO1 was significantly overexpressed in 19 cancer types compared to normal tissues. High SGO1 expression correlated with poorer overall survival (OS) and disease-free survival (DFS) in more than 10 cancers, validated by Kaplan-Meier analysis. Genomic analysis revealed frequent SGO1 mutations and DNA methylation dysregulation, while immune profiling showed associations with immune cell infiltration (B cells, CD8+ T cells) and PD-1/PD-L1 checkpoint genes. Protein-protein interaction and enrichment analyses uncovered BUB1 as a key co-expressed gene, suggesting a role in spindle checkpoint regulation. Functional assays in breast cancer cell line MDA-MB-231 and lung cancer cell line A549 showed SGO1 knockdown inhibited proliferation, migration, and invasion, with xenograft models confirming reduced tumor growth. Our findings establish SGO1 as a novel pan-cancer biomarker, linking its expression to tumor progression, immune evasion, and genomic instability. This study bridges bioinformatics with functional validation, offering new mechanistic insights and therapeutic avenues for SGO1-driven cancers.

Cuproptosis is a kind of programmed cell death in which copper reacts with the cycloaliphatic component of the tricarboxylic acid (TCA) cycle. In this study, we devised a predictive model and a theoretical framework to examine the variations in the expression of the cuproptosis-related genes (CRGs) in ovarian cancer. Through screening the 11 CRGs, all samples were segmented into two risk groups and a prognostic model was built. Among the 11 CRGs, 10 genes showed a significant relationship with survival probability, demonstrating the model had good prediction ability and high accuracy. Age and FIGO stage were discovered to be strongly correlated with patient survival time by means of univariate Cox regression analysis. The patients over 65 in FIGO stages IIIA-IV had an increased risk. The enrichment analysis showed that the main metabolic pathways were those related to drug metabolism, tissue development, tyrosine metabolism and retinol metabolism. The PPI networks revealed that CDKN2A was the key gene. Finally, the in vitro and in vivo functional assays demonstrated that cuproptosis induced by CuET agent treatment could significantly inhibit ovarian cancer cell viability, migration and invasion as well as xenografted tumor growth where the CDKN2A expression level increased. Our results indicate that the comprehensive definition of differentially expressed CRGs in ovarian cancer will provide new insights for clinical remedy of ovarian cancer.