Translational cancer research最新文献

Background: High-grade serious ovarian cancer (HGSOC) is a heterogeneous gynecological malignancy with high mortality, often diagnosed at advanced stages, and its prognosis remains poor despite therapeutic advances. Discovering innovative prognostic markers and developing predictive models are critical to improving treatment strategies in patients with HGSOC. Vasculogenic mimicry (VM), a tumor cell-derived vessel-like structure formation process, plays a critical role in tumor progression and is linked to poor prognosis, making it a potential target for prognostic biomarker development. In this study, we aimed to construct a VM-related prognostic risk model for HGSOC.

Methods: We analyzed transcriptomic and clinical data of HGSOC patients from the GSE9891 and The Cancer Genome Atlas (TCGA) database, identified VM-related genes, and developed a prognostic model using least absolute shrinkage and selection operator (LASSO)-Cox regression. The model's performance was validated via survival analysis, receiver operating characteristic (ROC) curves, and independent prognostic factor assessment in both training and test sets. Additionally, a nomogram integrating the model with clinical variables was established to optimize prognostic prediction. At the same time, we performed gene ontology (GO) analysis of differential genes in high- and low-risk HGSOC patients to obtain common enrichment pathways. Finally, after the survival analysis, we selected LRIG1 as a target gene and assessed the effect on tube formation using HGSOC cell lines OVCAR3.

Results: In this study, we systematically developed an HGSOC prognostic risk model founded on seven VM genes. Furthermore, we formulated a new nomogram that combines risk characteristics and clinical pathological features, which provided good predictive performance for the clinical prognosis of HGSOC patients. At the same time, we found that LRIG1 was a key gene related to a better prognosis of HGSOC and inhibited tube formation capacity of OVCAR3.

Conclusions: We identified VM-related genes, constructed a prognostic risk model for HGSOC, and found that LRIG1 was a prognostic factor for HGSOC.

Background: Abnormal chromatin remodeling figures prominently in the progression and treatment of malignancies. However, the prognostic significance of chromatin remodeling-related genes (CRRGs) in hepatocellular carcinoma (HCC) has not been extensively examined. Therefore, this study aimed to identify prognostic genes associated with chromatin remodeling in HCC and to determine their prognostic significance.

Methods: Differential expression analysis was conducted on The Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) dataset to identify differentially expressed genes (DEGs). By overlapping DEGs with CRRGs and performing enrichment analysis, we identified chromatin remodeling-related DEGs (CRR-DEGs). Risk models were developed via least absolute shrinkage and selection operator (LASSO) and Cox regression analyses and were validated in International Cancer Genome Consortium Liver Cancer-RIKEN, Japan (ICGC-LIRI-JP) dataset. Independent prognostic factors were screened out according to risk scores and clinical indicators, and a nomogram was created. Additional analysis included survival, expression, mutation, enrichment, and regulatory network predictions.

Results: Eighteen CRR-DEGs were identified through the intersection of DEGs and CRRGs, all of which were found to be enriched in adenosine triphosphate (ATP)-dependent chromatin remodeling pathways, and eight of which were found to be specifically enriched in HCC. Four prognostic genes (ACTR5, NFRKB, RBBP7, and RUVBL1) were selected to construct a risk model via LASSO and univariate Cox regression analyses, which was validated in the ICGC-LIRI-JP dataset. Calibration curves and receiver operating characteristic (ROC) curve analysis indicated the superior accuracy of the nomogram for predicting HCC with chromatin remodeling. Enrichment analysis linked the prognostic genes to pathways such as DNA replication, spliceosome, and cell cycle.

Conclusions: Four prognostic genes associated with chromatin remodeling in HCC were identified, and a prognostic model for HCC was established, offering valuable insights for the development of HCC treatment strategies.

Background: Invasive lobular carcinoma (ILC) accounts for 15% of breast cancers and presents challenges such as chemotherapy resistance and poorer survival outcomes compared to other subtypes. While often managed similarly to invasive ductal carcinoma (IDC), ILC requires tailored approaches due to its distinct biology. Ferroptosis, an iron-dependent form of cell death, shows potential in overcoming therapeutic resistance but remains unexplored in ILC. This study aimed to identify ferroptosis-related molecular subtypes, develop a robust gene signature using machine learning, construct an integrated prognostic model, and uncover potential therapeutic targets for ILC.

Methods: This study integrated data from a total of 490 patients with ILC across four datasets from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and Gene Expression Omnibus (GEO). The TCGA cohort was utilized for model development, while the remaining cohorts served for independent external validation. ILC samples were classified into two subtypes based on the expression levels of ferroptosis-related genes via consensus clustering. The associations between the subtypes and the tumor microenvironment (TME), biological function, and mutation were assessed. A ferroptosis-related gene signature (FRGS) was developed using the integration of machine learning. A prediction model was subsequently constructed by combining the FRGS with clinical features. Sensitivity analysis and molecular docking were used to identify potentially effective targets and drugs.

Results: We identified two ferroptosis-related subtypes and found that Cluster 2 had increased immune cell infiltration. By integrating machine learning, we identified 10 hub biomarkers of ILC and developed a FRGS. The FRGS was proven to be an independent risk factor for overall survival. Combining the FRGS with clinical features, a stable and superior ILC prognostic model was constructed. Sensitivity analysis and molecular docking revealed that KLRB1 and SERPINB5 are hypothesis-generating targets and that rapamycin and AZD5582 are hypothesis-generating drug candidates for the treatment of ILC.

Conclusions: By integrating multi-omics analysis, machine learning and molecular docking, we established a robust prognostic model for ILC, revealed two distinct ferroptosis-related molecular subtypes, and identified potential therapeutic targets and candidate drugs. These findings may help advance the development of personalized medicine and targeted therapies for ILC.

Background: Concurrent chemoradiotherapy (CCRT) following induction chemotherapy (IC) remains the conventional treatment regimen for patients with locally advanced nasopharyngeal carcinoma (LANPC). However, the complete response (CR) rate after IC is limited, and there is considerable heterogeneity in patient responses. The introduction of immunotherapy has shown potential to enhance treatment efficacy; nevertheless, there is a lack of reliable biological markers that can effectively predict whether induction immunochemotherapy will result in CR. Multiparametric magnetic resonance imaging (MRI) offers a non-invasive method to provide comprehensive information regarding tumor structure and function. This study aims to develop and validate a multimodal fusion model that integrates traditional MRI-based radiomics features with deep learning radiomics features to predict the early achievement of CR in patients with LANPC undergoing induction immunochemotherapy, thereby establishing a foundation for personalized treatment decisions.

Methods: We conducted a retrospective analysis of clinical and imaging data from 230 biopsy-confirmed LANPC patients who underwent induction immunochemotherapy at Guangxi Medical University Cancer Center between January 2021 and December 2024. The patients were randomly allocated into training (n=184) and testing (n=46) cohorts. Regions of interest (ROIs) for the lesions were delineated across multiple sequences, including T1-weighted imaging (T1), T2-weighted imaging (T2), and contrast-enhanced T1-weighted imaging (CE-T1). Traditional and deep learning radiomics features were extracted, followed by feature selection to identify the most discriminative features. Utilizing machine learning algorithms, we developed four types of models: clinical, traditional radiomics, deep learning radiomics, and multimodal fusion models. Model performance was evaluated through receiver operating characteristic (ROC) curve analysis, area under the curve (AUC), and decision curve analysis (DCA).

Results: The multimodal fusion model exhibited superior predictive performance in the testing cohort [AUC =0.844, 95% confidence interval (CI): 0.695-0.992], significantly outperforming both the traditional radiomics fusion model (AUC =0.721; 95% CI: 0.540-0.901) and the deep learning radiomics fusion model (AUC =0.725; 95% CI: 0.566-0.885).

Conclusions: The multimodal fusion model effectively predicts early CR in LANPC patients following induction immunochemotherapy, demonstrating significant potential for clinical application.

Background: Identifying predictive markers for immunotherapy in non-small cell lung cancer (NSCLC) is critical for personalized treatment. This study aimed to construct a predictive model that integrates clinical features, enhanced computed tomography (CT)-radiomics, and deep learning (DL) features for the assessment of durable clinical benefit (DCB) from immunotherapy in patients with advanced NSCLC and to provide biological interpretability to predictions by integrating radiogenomic data.

Methods: We conducted a retrospective analysis of 201 advanced NSCLC patients who underwent immunotherapy with CT images, with data supplemented from The Cancer Imaging Archive (TCIA). Radiomics features (RFs) were extracted from enhanced CT images, and DL features were derived using a pre-trained ResNet-34 model. DCB-related signatures were constructed using the least absolute shrinkage and selection operator (LASSO) algorithm, and fusion nomogram models were developed by integrating significant clinical variables, radiomics, and DL features. Shapley additive explanations were employed to quantify the impact of radiomics-DL features on model predictions. Gene set enrichment and biological correlation analyses based on transcriptomic TCIA data were performed to explore the biological significance of radiomics-DL score.

Results: Statistically significant clinical predictors included initial efficacy, brain metastases, programmed death-ligand 1 (PD-L1) expression, and hemoglobin levels. The fusion nomogram model demonstrated the highest predictive accuracy for DCB, with area under the curve (AUC) values of 0.843 in the train cohort and 0.894 in the test cohort, surpassing individual feature sets. Biological exploration revealed associations between radiomics-DL score and biological characteristics, including immune responses and immunoregulation.

Conclusions: This integrated approach shows the potential of combining clinical, radiomics and deep learning features (DLFs) as a noninvasive biomarker for predicting immunotherapy efficacy in NSCLC, assisting in patient selection and clinical decision-making. Radiotranscriptomic analysis may reveal key cellular and immune patterns associated with radiomics-DL signature.

Background: Head and neck squamous cell carcinoma (HNSCC) remains an aggressive malignancy with a poor prognosis, necessitating the identification of novel therapeutic targets and molecular mechanisms. Ferroptosis, an iron-dependent form of programmed cell death, is implicated in tumor progression. COL10A1 is dysregulated in various cancers, but its role and mechanism in HNSCC are unclear. This study aimed to investigate the expression, clinical significance, and functional mechanism of COL10A1 in HNSCC, focusing on its interaction with ANXA5 and regulation of ferroptosis.

Methods: COL10A1 expression was measured in HNSCC tissues and cell lines using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blot, and its expression in paraffin-embedded HNSCC tissues was detected by immunohistochemistry. The protein ANXA5, which interacts with COL10A1, was screened, and the effects of COL10A1 and ANXA5 on HNSCC cells were analyzed. GPX4 expression was detected by immunofluorescence, RT-qPCR and Western blot. Reactive oxygen species in HNSCC cells were detected by flow cytometry, and the ratio of reduced glutathione (GSH) to oxidized glutathione (GSSG) was measured by enzyme-linked immunoassay.

Results: COL10A1 expression was increased in HNSCC cells and cancer tissues. Interference with COL10A1 expression inhibited HNSCC cell proliferation, migration, and invasion and enhanced apoptosis. ANXA5, found to be increased in HNSCC, interacts with COL10A1. Interference with COL10A1 or ANXA5 expression suppressed malignant progression and promoted ferroptosis of HNSCC cells.

Conclusions: COL10A1 is expressed abnormally in HNSCC and modulates cell proliferation, migration, invasion, apoptosis, and ferroptosis through its interaction with ANXA5.

Background: Hepatocellular carcinoma (HCC) exhibits notable characteristics, encompassing frequent recurrence, weak immunotherapeutic outcomes and unfavorable prognosis. BTF3L4 has been identified as a critical factor in the progression of various malignancies. However, its specific role in HCC remains to be elucidated. This investigation sought to examine BTF3L4 levels in HCC and BTF3L4's connection with clinical prognosis and immune infiltration.

Methods: We performed an extensive multi-omics evaluation in the course of our research. Bioinformatics tools were utilized to assess BTF3L4 messenger RNA (mRNA) expression in HCC. Multiplex immunohistochemistry (mIHC) was utilized to examine BTF3L4 protein expression and to explore its correlation with tumor-infiltrating immune cells (TIICs). Cox regression analysis and Kaplan-Meier survival curves were applied to determine BTF3L4's impact on patient outcomes.

Results: Our analysis revealed markedly elevated levels of both BTF3L4 mRNA and protein in HCC tissues. BTF3L4 protein abundance emerged as an independent predictor of reduced survival in patients with HCC. Furthermore, elevated BTF3L4 protein expression was positively associated with cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) expression and markedly negatively correlated with CD4⁺ T cells and CD66b⁺ neutrophils in HCC tissues.

Conclusions: This evidence indicates that BTF3L4 functions as a predictive indicator and is a potential candidate for HCC immunotherapy.

Background: High-grade serous ovarian cancer (HGSOC) constitutes the deadliest form of gynecologic tumor, with its high invasiveness and peritoneal dissemination closely associated with epigenetic regulation. HOXB8 has been implicated in tumor-promoting functions, but its role in regulating the KDM6B/C/EBPα signaling axis in HGSOC metastasis has not been fully elucidated. Here, we examined how HOXB8 regulates this pathway and downstream CCND1 expression, as well as its impact on ovarian cancer cell invasion and migration.

Methods: SKOV3 human ovarian cancer cells were subjected to HOXB8 knockdown or overexpression via small interfering RNA (siRNA) transfection and plasmid-mediated gene delivery. Functional rescue assays were performed with KDM6B-specific siRNA and the H3K27me3 methyltransferase inhibitor GSK126. The expression of KDM6B, C/EBPα, CCND1, and overall H3K27me3 was examined by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. Levels of inflammatory cytokines were determined using enzyme-linked immunosorbent assay (ELISA), while cellular growth, motility, and invasive ability were evaluated through Cell Counting Kit-8 (CCK-8), Transwell, and wound-healing assays.

Results: HOXB8 overexpression significantly downregulated KDM6B and C/EBPα expression, upregulated CCND1 and H3K27me3 levels, increased tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, and C-reactive protein (CRP) secretion, and markedly enhanced cellular growth, motility, and invasive ability. HOXB8 knockdown produced the opposite effects. KDM6B silencing phenocopied the pro-invasive effects of HOXB8 overexpression. In contrast, administration of GSK126 partly counteracted the reduction of C/EBPα and the increase of CCND1 triggered by KDM6B depletion, while also attenuating cytokine secretion and invasive capacity.

Conclusions: HOXB8 promotes inflammatory responses and metastatic potential in HGSOC cells by suppressing the KDM6B/C/EBPα signaling axis, inducing aberrant H3K27me3 modification, and upregulating CCND1. Targeting HOXB8-mediated pathways may provide novel therapeutic opportunities for limiting ovarian cancer progression.

Background: Primary pulmonary lymphoma (PPL) is a rare lymphoproliferative disorder with unclear optimal management strategies. The prognostic impact of cancer-directed surgery (CDS) remains controversial, with existing evidence limited to small institutional series yielding conflicting conclusions. This study aimed to evaluate the association between CDS and survival outcomes in patients with PPL using a large population-based cohort.

Methods: Patients diagnosed with PPL between 2000 and 2014 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was employed to minimize confounding. Overall survival (OS) and cancer-specific survival (CSS) were assessed using Kaplan-Meier analysis and multivariable Cox regression with corrected Akaike information criterion (AICc)-based model averaging.

Results: A total of 2,782 patients with PPL were identified, of whom 889 (32.0%) underwent CDS. After 1:1 PSM, 727 well-balanced pairs were generated. In the matched cohort, patients who underwent CDS demonstrated significantly superior CSS (P<0.001) and OS (P<0.001) compared with those who did not receive CDS. Multivariable Cox regression confirmed CDS as an independent favorable prognostic factor for both CSS (hazard ratio 0.63, 95% confidence interval: 0.51-0.77) and OS. Subgroup analyses revealed that CSS and OS benefits associated with CDS were most pronounced among patients aged 60 years or younger, females, married individuals, those with Ann Arbor stage I-II disease, and those who received radiotherapy. Among histologic subtypes, patients with mucosa-associated lymphoid tissue (MALT) lymphoma derived a particular CSS benefit from CDS.

Conclusions: CDS was associated with improved survival in patients with PPL, particularly among younger patients with early-stage disease and MALT lymphoma. These findings support consideration of surgery for selected patients with resectable PPL, although prospective validation is needed.