Journal of Cancer最新文献

Introduction: Appropriate patient selection is essential for optimising outcomes in individuals with peritoneal metastasis (PM) undergoing treatment with Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC). This study investigated the prognostic value of pretreatment inflammatory biomarkers and explored their ability to predict the possibility of completion of three or more PIPAC treatments. Method: This observational study analysed prospectively collected data from patients with PM of gastrointestinal or ovarian origin enrolled in the PIPAC OPC-1 or OPC-2 studies between March 2015 and January 2022. Six biomarkers were examined: Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio, Systemic Immune-Inflammation Index (SII), C-reactive protein, modified Glasgow Prognostic Score, and Prognostic Nutritional Index. Biomarkers were obtained pretreatment, and treated as continuous variables. Survival was assessed using Kaplan-Meier and Cox regression analyses, adjusting for covariates available prior to the first PIPAC. ROC analysis was used to evaluate the predictive performance. A p-value less than 0.05 was considered statistically significant. Results: The cohort consisted of 130 patients, with a median overall survival (OS) of 8.7 months. Sixty percent of the patients received three or more PIPAC treatments. Elevated levels of all six biomarkers were significantly associated with reduced OS. In the multivariate analysis, five biomarkers remained independently associated with survival. NLR and SII demonstrated moderate discriminatory power (AUC > 0.70) for predicting the completion of three or more treatments. Conclusion: Pretreatment inflammatory biomarkers are objective, readily accessible and clinically applicable tools that may support the selection of appropriate candidates for PIPAC. The findings of this study encourage the integration of biomarker assessments into future PIPAC research protocols.

Rhabdomyosarcoma (RMS) is a rare disease that arises from skeletal muscle mainly affects children and adolescents. Patients with RMS have diverse symptoms and prognosis based on tumor sizes, tumor anatomical locations, histological subtypes of the tumors and genetic testing of paired-box-forkhead box O1 (PAX-FOXO1) fusion gene. The 4 subtypes of RMS include embryonal RMS (eRMS), alveolar RMS (aRMS), spindle cell/sclerosing RMS (scRMS) and pleomorphic RMS (pRMS). Treatment for RMS patients remains challenging due to its heterogeneous nature. Thus, a combinatory approach is likely to warrant better management of RMS. Given that PAX-FOXO1 fusion gene is the most common biomarker for RMS, though this fusion gene only accounts for 16-20% of RMS patients. Targeted therapy that tailors treatment plans to the individual patient may provide additional benefits for RMS patients. This review describes the frequent genetic mutations observed in RMS patients and drug development based on these mutations shall provide direction to develop targeted therapy leading to effective personalized treatment for RMS patients.

Gastrointestinal tumors are among the most common tumors worldwide and are currently the leading cause of cancer-related deaths. Gastrointestinal tumors affect the digestive system and include esophageal, liver, gastric, colorectal, and pancreatic cancers. Aerobic glycolysis is a widespread phenomenon among gastrointestinal tumor cells, which poses a major hazard to human health and life. Increasing evidence suggests that aerobic glycolysis can induce and promote the development of gastrointestinal tumors by rapidly providing large amounts of energy and altering the tumor microenvironment. Among them, glucose transporter proteins and key enzymes involved in glycolysis are expressed at higher levels during aerobic glycolysis, and the corresponding signaling pathways and transcription regulatory factors are activated, playing an important role in the occurrence and development of tumors. Additionally, evidence has indicated that aerobic glycolysis plays an essential role in inhibiting the development of immune cells, modifying the population of immune cells present in the surrounding tumor, and promoting the polarization of immune cells. Moreover, drugs and compounds that target essential enzymes and transcription factors associated with glycolysis are known to exhibit anticancer properties.

Cuproptosis, a newly identified form of cell death, influences the development, progression, and prognosis of prostate cancer (PCa). Identifying key genes associated with cuproptosis and developing robust predictive models through machine learning approaches are crucial for personalized PCa treatment. In our study, multiple machine learning methods and their combinations were employed for the construction of diagnostic and prognostic models for PCa, which were then validated in multiple external independent cohorts. The model key gene, PROK1, was selected for further analysis, and its expression was compared in clinical samples and cell lines. Additionally, the anticancer effect of PROK1 was explored through regulating the expression of PROK1. Most cuproptosis-related genes (CRGs) showed differential expression between PCa and normal prostate tissues. The two clusters derived from the Consensus Clustering method, based on cuproptosis gene expression characteristics, exhibit distinct clinical features and immune microenvironment infiltration patterns. Models constructed based on machine learning methods showed promising diagnostic capabilities for PCa and were associated with the prediction of biochemical recurrence-free survival and disease-free survival of patients. Inhibiting PROK1 expression promoted PCa cell proliferation and invasion, while its overexpression had the opposite effect. Furthermore, pathway exploration revealed that PROK1 inhibits tumor growth by mediating apoptosis under copper ion stress. Its association with cuproptosis warrants further investigation to elucidate the precise mechanism.

Exostosin glycosyltransferase 1 (EXT1) and exostosin glycosyltransferase 2 (EXT2) catalyze heparan sulfate chain elongation and are increasingly implicated in cancer biology, but their roles in gliomas remain incompletely defined. Here, we performed an integrative multi-omics analysis to dissect the transcriptional, epigenetic, and microenvironmental landscape of EXT1 and EXT2 across gliomas. Bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) revealed that both EXT1 and EXT2 are upregulated in high-grade gliomas and associate with adverse survival, with EXT1 showing the strongest and most consistent prognostic impact. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) indicated that EXT1-high tumors are enriched for DNA damage and replication stress programs, cell cycle progression, inflammatory response, and stromal activation pathways, whereas EXT2 expression is preferentially linked to extracellular matrix remodeling, cytoskeletal organization and angiogenesis-related signaling. Single-cell RNA sequencing and Immune deconvolution using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) showed that EXT1 correlates with increased stromal and immune scores, and reduced cytotoxic T cell signatures, consistent with an immunosuppressive tumor microenvironment. EXT2 expression is enriched in gliomas with pronounced vascular and mesenchymal features, supporting a complementary role in invasive growth and tissue remodeling. Immunohistochemistry on a glioma tissue microarray validated the upregulation of EXT1 protein in high-grade tumors. The study findings identified EXT1 as a central glycosylation-linked regulator of replication stress tolerance and immune remodeling in gliomas, and suggest that EXT2 contributes to extracellular matrix and cytoskeletal reprogramming. The exostosin axis represents a promising source of prognostic biomarkers and potential therapeutic targets in glioma.

Background: Alpha-fetoprotein (AFP), Des-gamma carboxy-prothrombin (DCP), lectin-bound AFP (AFP-L3) and Golgi protein-73 (GP73) have been used or proposed as surveillance tests for hepatocellular carcinoma (HCC). The aims of this study were to determine the performance of AFP, DCP, AFP-L3, GP73 and their combination in the diagnosis and prognosis of HCC. Methods: A total of 578 patients were enrolled, including 303 HCC patients, 104 patients with liver cirrhosis, 101 patients with chronic hepatitis and 70 healthy volunteers. The serum levels of AFP, DCP, AFP-L3 and GP73 were quantified before treatment, 7 days and 30 days after treatment. Results: AFP had the best area under the curve (AUC = 0.850), followed by DCP (0.775) and AFP-L3 (0.763), for the prediction of HCC, whereas GP73 had low diagnostic value (0.549). The combination of AFP, DCP and AFP-L3 significantly improved diagnostic performance (AUC = 0.895). The level of AFP 30 days after treatment had the best predictive value for HCC recurrence (AUC = 0.779). Higher recurrence rates were associated with an increasing number of elevated tumor markers measured both before and 30 days after treatment. Furthermore, patients whose marker status remained positive 30 days after treatment had a higher recurrence rate than patients whose marker status changed to negative. Conclusions: AFP was more effective than DCP and AFP-L3 for the diagnosis and prognosis of HCC, and the combination of AFP, AFP-L3 and DCP enhanced the diagnostic performance. The dynamic changes in biomarker positive status after treatment and the number of positive biomarkers play important roles in predicting HCC recurrence.

Immune checkpoint inhibitors are effective treatments for many tumors. However, existing biomarkers can benefit only a small selection of colorectal cancer patients. Super-enhancers are associated with various tumor characteristics. We wondered whether super-enhancer-related genes could be novel biomarkers for immunotherapy. We screened super-enhancer-related genes that were highly correlated with immune infiltration through weighted gene co-expression network analysis on the basis of chromatin immunoprecipitation sequencing data. A prognostic risk signature was established using least absolute shrinkage and selection operator and cox regression models. By analyzing the correlations between the expression of model genes and the immunophenotypic and microsatellite instability scores, we determined that PLAU and GSDMC expression had high predictive value for immunotherapy efficacy. Moreover, we predicted the sensitivity of the PLAU and GSDMC proteins to drugs by virtual docking. Finally, we validated the effect of the super-enhancer activity on PLAU and GSDMC expression. Overall, our study identified super-enhancer-based biomarkers for predicting survival and immunotherapy efficacy in colorectal cancer.

Cancer cell dormancy is associated with tumor recurrence and metastasis. Chemotherapy usually induces dormancy as external pressure on the tumor. Dormant cells have considerable resistance to antitumor drugs, although they are not harmful to the host if they do not wake up. Chemotherapy induces a dormant phenotype through remodeling of the tumor microenvironment and alteration of intracellular signaling networks. Multiple adaptive mechanisms that confer drug resistance have been identified in these dormant cells, including the unfolded protein response to endoplasmic reticulum stress, metabolic reprogramming favoring oxidative phosphorylation to avoid damage from oxidative stress, and autophagy to realize the circular utilization of energy. However, dormancy is reversible. The conversion between dormancy and awakening of the tumor during chemotherapy and the recovery period after treatment is modulated by several factors, including the dose and cycle of treatment, and individual differences among patients. The direct elimination of cancer cells or permanent dormancy by chemotherapy predicts favorable outcomes. According to this theory, understanding the mechanisms of cancer dormancy and awakening under chemotherapy and improving prognosis using suitable treatment strategies requires further investigation. This review analyzed studies on cancer cell dormancy and response to chemotherapy to identify potential novel interests for future studies and probable strategies to optimize chemotherapy in clinical trials.

Metabolic reprogramming is an important feature in non-small cell lung cancer (NSCLC) that can result in therapeutic resistance. Exploring dysregulated lipid metabolism in NSCLC will accelerate the development of potential lipid biomarkers to target and control the malignant progression of NSCLC. In this study, RNA next-generation sequencing of 25 paired NSCLC specimens and adjacent normal tissues was used to find that carboxylesterase 3 (CES3) was upregulated in NSCLC. Knockdown of CES3 significantly inhibited NSCLC cell proliferation and invasion. Additionally, CES3 inhibition promoted lipid accumulation in NSCLC cells. Furthermore, we found transcription factor AP-2α (TFAP2A) could regulate CES3 levels in NSCLC. TFAP2A was found upregulated in NSCLC and correlated with poorer outcome. Inhibiting TFAP2A resulted in suppressed cell proliferation as well as invasion while increasing the lipid accumulation in NSCLC. CES3 overexpression could reverse the impact of TFAP2A inhibition on NSCLC progression. In summary, TFAP2A dysregulation resulted in CES3 overexpression and the following NSCLC tumorigenesis. Targeting the TFAP2A/CES3 axis may represent a promising therapeutic strategy for NSCLC in the future.

Colorectal cancer (CRC) is the third most common malignancy and leading cause of mortality worldwide. Tumor microenvironment (TME) strongly influences CRC growth, immune evasion, and metastasis. Among various immune cells, tumor-associated macrophages (TAMs) act as key regulators of cancer progression. Although traditionally classified as M1 (pro-inflammatory, anti-tumor) or M2 (anti-inflammatory, pro-tumor), single-cell RNA sequencing and spatial transcriptomics have revealed that macrophage phenotypes exist along a continuum, challenging the classic dichotomy. This review investigates macrophages throughout CRC development, from normal mucosa to adenoma, primary tumor, and liver metastasis. Early adenomas feature M1-like macrophages that drive local inflammation, whereas advanced adenomas and invasive CRC comprise M2-like macrophages promoting angiogenesis, extracellular matrix remodeling, and immunosuppression. TAMs are crucial in CRC metastasis, particularly to the liver. M2-polarized Kupffer cells express CD206 and CD163, secrete hepatocyte growth factor, and activate PI3K/AKT signaling, thus aiding extravasation, survival, and proliferation of metastatic cells. They also foster lymphangiogenesis and immunosuppression through release of IL-10 and TGF-β. CRC's consensus molecular subtype (CMS) impacts the profile of TAMs: CMS1 (microsatellite instability-high) tumors typically harbor an anti-tumor M1 macrophages, while CMS4 (mesenchymal) tumors are enriched in M2-like TAMs, which facilitate stromal remodeling and angiogenesis, ultimately contributing to a poor prognosis. Spatial distribution also matters. Abundant M1 macrophages at the invasive margin correlate with better outcomes, whereas M2 macrophages in tumor centers and metastatic sites drive disease progression. Some CD206+ macrophages, however, support vascular normalization, which can limit metastasis. These findings underscore the complexity of TAMs in CRC and highlight the necessity of multi-marker phenotyping. Given the limitations of the M1/M2 paradigm, advanced techniques such as spatial transcriptomics and single-cell RNA sequencing offer novel insights into TAM heterogeneity. Future therapeutic strategies targeting TAMs, including metabolic reprogramming, epigenetic modulators, and immune checkpoint inhibitors, hold promise for improving CRC patient outcomes by shifting the balance toward an anti-tumor immune response.